diff --git a/pydata-yerevan-2022/category.json b/pydata-yerevan-2022/category.json new file mode 100644 index 000000000..4a9de62b2 --- /dev/null +++ b/pydata-yerevan-2022/category.json @@ -0,0 +1,3 @@ +{ + "title": "PyData Yerevan 2022" +} diff --git a/pydata-yerevan-2022/videos/aghasi-tavadyan-the-dangers-of-mindless-forecasting-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/aghasi-tavadyan-the-dangers-of-mindless-forecasting-pydata-yerevan-2022.json new file mode 100644 index 000000000..40543e369 --- /dev/null +++ b/pydata-yerevan-2022/videos/aghasi-tavadyan-the-dangers-of-mindless-forecasting-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Aghasi Tavadyan Presents:\n\nThe Dangers of Mindless Forecasting\n\n\"Prediction is very difficult, especially if it\u2019s about the future!\" This phrase is attributed to Niels Bohr, the Nobel laureate in Physics and father of the atomic model. This quote warns about the unreliability of forecasts without proper testing and about constant changes in the initial assumed conditions.\n\nWith modern programming languages and convenient packages that provide ready-made modeling solutions, it is often easy to find a model that fits the past data well; perhaps too well! But does the maximization of metrics justify the means? Should the complex structures of predictions be built on the quicksand of noisy data?\n\nThis talk is a laid-back discussion that will be useful for the audience from any background, from beginner to advanced. Aghasi Tavadyan is the founder of Tvyal.com, which translates to \"data\" from Armenian. You can find more info about him following these websites: tavadyan.com, tvyal.com.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Aghasi-Tavadyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1930, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Aghasi-Tavadyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Aghasi-Tavadyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/NGYR_f8HlQg/maxresdefault.jpg", + "title": "Aghasi Tavadyan - The Dangers of Mindless Forecasting | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=NGYR_f8HlQg" + } + ] +} diff --git a/pydata-yerevan-2022/videos/aleksandr-patrushev-use-automl-to-create-high-quality-models-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/aleksandr-patrushev-use-automl-to-create-high-quality-models-pydata-yerevan-2022.json new file mode 100644 index 000000000..e5f748faf --- /dev/null +++ b/pydata-yerevan-2022/videos/aleksandr-patrushev-use-automl-to-create-high-quality-models-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Aleksandr Patrushev Presents:\n\nUse AutoML to Create High-Quality Models\n\nAWS provides a range of AutoML solutions for all levels of expertise. In this session, we will cover AutoGluon, a library for ML practitioners seeking an open source solution, and Amazon SageMaker tool for data scientists who prefer a fully-managed service. Developers or business users without ML experience can take advantage of ready-made solutions for use cases such as computer vision, demand forecasting, intelligent search, and industrial and healthcare verticals.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Aleksandr-Patrushev.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2464, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Aleksandr-Patrushev.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Aleksandr-Patrushev.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/q-gFWt1Msrc/maxresdefault.jpg", + "title": "Aleksandr Patrushev - Use AutoML to Create High-Quality Models | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=q-gFWt1Msrc" + } + ] +} diff --git a/pydata-yerevan-2022/videos/alex-laptev-nvidia-nemo-toolkit-for-conversational-ai-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/alex-laptev-nvidia-nemo-toolkit-for-conversational-ai-pydata-yerevan-2022.json new file mode 100644 index 000000000..28be0e4de --- /dev/null +++ b/pydata-yerevan-2022/videos/alex-laptev-nvidia-nemo-toolkit-for-conversational-ai-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Alex Laptev Presents:\n\nNVIDIA NeMo: Toolkit for Conversational AI \n\nConversational AI is a technology that allows a \u201cmachine\u201d to speak to a person in a natural language. NVIDIA NeMo is an open-source conversational AI toolkit built for researchers working on automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech synthesis (TTS). The primary objective of NeMo is to help researchers from industry and academia to develop new models for automatic speech recognition, text-to-speech, natural language processing, and neural machine translation. Nemo also has a large number of step-by-step tutorials and pre-trained models.\n\nThe outline of the talk goes as follows:\n1. NeMo overview.\n2. Where to start: tutorials on ASR, TTS, and NLP.\n3. NeMo ASR overview.\n4. NeMo TTS overview.\n5. NeMo NLP overview.\n6. From research to production: deploying NeMo models.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Alex-Laptev.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2174, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Alex-Laptev.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Alex-Laptev.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/J-P6Sczmas8/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXyhlMA8=&rs=AOn4CLAHMJorD1oa5sv5TUXkVDclkHA5fA", + "title": "Alex Laptev - NVIDIA NeMo: Toolkit for Conversational AI | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=J-P6Sczmas8" + } + ] +} diff --git a/pydata-yerevan-2022/videos/andrey-manoshin-hayk-aprikyan-lightning-talks-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/andrey-manoshin-hayk-aprikyan-lightning-talks-pydata-yerevan-2022.json new file mode 100644 index 000000000..219505e90 --- /dev/null +++ b/pydata-yerevan-2022/videos/andrey-manoshin-hayk-aprikyan-lightning-talks-pydata-yerevan-2022.json @@ -0,0 +1,51 @@ +{ + "description": "Andrey Manoshin Presents:\n\nEENLP: Cross-lingual Eastern European NLP Index\n\nIn our project we present a wide index of existing Eastern European language datasets (90+) and models (60+). Furthermore, to support the evaluation of commonsense reasoning tasks, we compile and publish cross-lingual datasets for five such tasks and provide evaluation results for several existing multilingual models.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Andrey-Manoshin.pdf\n--\nHayk Aprikyan Presents:\n\nWhat can your Telegram tell about you? (Answer: Everything)\n\nHow much has your vocabulary changed over the last year? Who shares the funniest memes with you? And does she find you interesting to chat with? \u0336N\u0336o\u0336p\u0336e\u0336.\u0336\n\nIf you're a Telegram guy, Neplo is that painstakingly data-driven guy who's got answers to these (and hundreds of other) questions based on your Telegram chat histories.\n\nStill skeptical? Come and see. (John 1:39)\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Hayk-Aprikyan.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2095, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Hayk-Aprikyan.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Hayk-Aprikyan.pdf" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Andrey-Manoshin.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Andrey-Manoshin.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi_webp/y7_MlSSh7Co/maxresdefault.webp", + "title": "Andrey Manoshin, Hayk Aprikyan | Lightning Talks | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=y7_MlSSh7Co" + } + ] +} diff --git a/pydata-yerevan-2022/videos/anna-shahinyan-cifar-10-exploratory-data-analysis-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/anna-shahinyan-cifar-10-exploratory-data-analysis-pydata-yerevan-2022.json new file mode 100644 index 000000000..16bc4810f --- /dev/null +++ b/pydata-yerevan-2022/videos/anna-shahinyan-cifar-10-exploratory-data-analysis-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Anna Shahinyan Presents:\n\nCifar-10 Exploratory Data Analysis\n\nImage classification datasets are completed from the analysis point of view, taking into account the complicated structure of images. However, the understanding of the dataset descriptors at the high level can add debugging facilities and, in early stage, predict the quality of the classification model. During this session, we will visually analyze one of the challenging SOTA datasets like Cifar-10.\nThe datasets in AI used to contain 1000+ images. Images are matrices, and the handling of available features, missing features that can lead to AI model overfitting or underfitting. Based on visualization will predict whether we can reduce the dataset and come up with a smaller set and predict its impact on the final AI model.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Anna_Shahinyan_16_9_pydata_2022.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1656, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Anna_Shahinyan_16_9_pydata_2022.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Anna_Shahinyan_16_9_pydata_2022.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/UfJh_5ea0EQ/maxresdefault.jpg", + "title": "Anna Shahinyan - Cifar-10 Exploratory Data Analysis | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=UfJh_5ea0EQ" + } + ] +} diff --git a/pydata-yerevan-2022/videos/anush-tosunyan-tiling-parallel-processing-of-large-images-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/anush-tosunyan-tiling-parallel-processing-of-large-images-pydata-yerevan-2022.json new file mode 100644 index 000000000..2f72ba92c --- /dev/null +++ b/pydata-yerevan-2022/videos/anush-tosunyan-tiling-parallel-processing-of-large-images-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Anush Tosunyan Presents:\n\nTiling & Parallel Processing of Large Images \n\nDuring this session, we will review the benefits of processing large imageries by tiles, review use cases, and later combination of results. \nAs previously mentioned we will review the benefits of tile level processing for large imageries, going further into some use cases seen in data analysis, software engineering, and ML models(such as classification and segmentation). We will review how the tile data was later combined for each use case separately, and what were the benefits we saw from adopting this approach.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Anush-Tosunyan.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1468, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Anush-Tosunyan.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Anush-Tosunyan.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/bf11u4o8tEw/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGGUgYihWMA8=&rs=AOn4CLAYt07v1EZGR8A4atLIr9-CYdns9Q", + "title": "Anush Tosunyan - Tiling & Parallel Processing of Large Images | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=bf11u4o8tEw" + } + ] +} diff --git a/pydata-yerevan-2022/videos/arpi-sahakyan-how-to-start-critical-thinking-in-data-science-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/arpi-sahakyan-how-to-start-critical-thinking-in-data-science-pydata-yerevan-2022.json new file mode 100644 index 000000000..aa5645143 --- /dev/null +++ b/pydata-yerevan-2022/videos/arpi-sahakyan-how-to-start-critical-thinking-in-data-science-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Arpi Sahakyan Presents:\n\nHow to Start Critical Thinking in Data Science \n\nThe aim of the presentation is to address issues concerning bias in data, misleading statistics, issues in testing, and other matters that are prevalent in the field of data science.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Arpi-Sahakyan.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2202, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Arpi-Sahakyan.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Arpi-Sahakyan.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/UXBBeNg5YUw/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEQgXihlMA8=&rs=AOn4CLB2fZGAbowoTh-fe38CZskHcG9Rxw", + "title": "Arpi Sahakyan - How to Start Critical Thinking in Data Science | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=UXBBeNg5YUw" + } + ] +} diff --git a/pydata-yerevan-2022/videos/artem-terentyuk-bert-model-for-real-world-healthcare-data-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/artem-terentyuk-bert-model-for-real-world-healthcare-data-pydata-yerevan-2022.json new file mode 100644 index 000000000..74a3eb58f --- /dev/null +++ b/pydata-yerevan-2022/videos/artem-terentyuk-bert-model-for-real-world-healthcare-data-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Artem Terentyuk Presents:\n\nBERT Model for Real World Healthcare Data\n\nEarly indication and detection of diseases, can provide patients with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning provide a great opportunity to address this unmet need. In this lecture, we introduce modified BERT: A deep neural sequence transduction model designed for electronic health records (EHR). We will consider the application of this methodology to the task of classifying patients into cohorts reflecting different disease patterns.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Artem-Terentyuk.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1210, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Artem-Terentyuk.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Artem-Terentyuk.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/XY53QdT6tWY/maxresdefault.jpg", + "title": "Artem Terentyuk - BERT Model for Real World Healthcare Data | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=XY53QdT6tWY" + } + ] +} diff --git a/pydata-yerevan-2022/videos/ashot-vardanian-accelerated-data-science-libraries-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/ashot-vardanian-accelerated-data-science-libraries-pydata-yerevan-2022.json new file mode 100644 index 000000000..1e7dba48b --- /dev/null +++ b/pydata-yerevan-2022/videos/ashot-vardanian-accelerated-data-science-libraries-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Ashot Vardanian Presents:\n\nAccelerated Data Science Libraries\n\nEveryone knows and uses Pandas, NumPy, and NetworkX, but is there something better? Something equally easy to use, but hopefully with more features or, more importantly, higher performance!\nIt is 2022, and we need to process a lot of data fast, but how? You can switch the BLAS version in NumPy. You can take CuPy instead to get GPU acceleration for Linear Algebra. You can replace NetworkX with RetworkX and cuGraph. You can replace Pandas with Modin, cuDF, or Dask. All of that is easy but comes with different pitfalls and hidden inefficiencies. \n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Ashot-Vardanian.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2329, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Ashot-Vardanian.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Ashot-Vardanian.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/OxAKSVuW2Yk/maxresdefault.jpg", + "title": "Ashot Vardanian - Accelerated Data Science Libraries | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=OxAKSVuW2Yk" + } + ] +} diff --git a/pydata-yerevan-2022/videos/daniel-kornev-building-your-own-multiskill-ai-assistant-with-deeppavlov-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/daniel-kornev-building-your-own-multiskill-ai-assistant-with-deeppavlov-pydata-yerevan-2022.json new file mode 100644 index 000000000..8ae46ae3a --- /dev/null +++ b/pydata-yerevan-2022/videos/daniel-kornev-building-your-own-multiskill-ai-assistant-with-deeppavlov-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Daniel Kornev Presents:\n\nBuilding your own Multiskill AI Assistant with DeepPavlov\n\nDid you ever dream of having your own AI assistant? Did you find yourself limited by Amazon Alexa or Google Assistant? Did you want to build yours for yourself or your company? \n\nIn this talk, you will learn how to build your own multiskill AI Assistant using the modern NLP techniques from DeepPavlov.ai. DeepPavlov is a well-known Conversational AI lab that has participated twice in the Amazon Alexa Prize Challenge, organized Conversational AI Challenges at NeurIPS, and built its very own open-source Conversational AI Stack.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Daniel-Kornev.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2279, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Daniel-Kornev.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Daniel-Kornev.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/yYIjQqPsnm0/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGGUgUihFMA8=&rs=AOn4CLBKccm4IPnVetU-3IcU4cpj-zMRYA", + "title": "Daniel Kornev - Building your own Multiskill AI Assistant with DeepPavlov | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=yYIjQqPsnm0" + } + ] +} diff --git a/pydata-yerevan-2022/videos/davit-abgaryan-recommendation-systems-in-market-research-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/davit-abgaryan-recommendation-systems-in-market-research-pydata-yerevan-2022.json new file mode 100644 index 000000000..ff2beaef8 --- /dev/null +++ b/pydata-yerevan-2022/videos/davit-abgaryan-recommendation-systems-in-market-research-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Davit Abgaryan Presents:\n\nRecommendation Systems in Market Research \n\nGathering opinion data at scale in market research assumes a platform where many users complete surveys from many different providers. As a result, the problem of matching surveys with users arises. Taking into account specifics of the market research industry, recommendation systems, multicriteria optimization, and regression models become a crucial part of efficient user-survey matching at scale.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Davit-Abgaryan.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2572, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Davit-Abgaryan.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Davit-Abgaryan.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/8fmUqkE7M_0/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGGQgZShTMA8=&rs=AOn4CLBT-ogHr3yW1dsUzjYpJM1lmyCaGA", + "title": "Davit Abgaryan - Recommendation Systems in Market Research | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=8fmUqkE7M_0" + } + ] +} diff --git a/pydata-yerevan-2022/videos/diyar-mohammadi-near-duplicate-ad-detection-in-online-classified-ad-services-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/diyar-mohammadi-near-duplicate-ad-detection-in-online-classified-ad-services-pydata-yerevan-2022.json new file mode 100644 index 000000000..324e7fd4c --- /dev/null +++ b/pydata-yerevan-2022/videos/diyar-mohammadi-near-duplicate-ad-detection-in-online-classified-ad-services-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Diyar Mohammadi Presents:\n\nNear-Duplicate Ad Detection in Online Classified Ad Services\n\nNear-Duplicate Ads are harmful to online classified ad services in many ways.\nDemand-side users face low-quality listings, which increases the time and effort required to find desired ads. Also, normal supply-side users get fewer views and make less profit out of their ads. Finally, Duplicate ads may cause a direct decrease in the business\u2019s revenue (By skipping payments such as ad boosting)\n\nIn this talk, first, we will discuss the problem, definition, metrics, and training data generation. Then, we will talk about modeling, feature engineering, and how step-by-step metrics were improved.\n\nFor texts, we have tried different approaches such as MinHash, CountVectorizer, Bi-LSTM, and transformers. For images, we have tried different approaches, such as Perception-Hash and CNNs.\n\nAlso, we will discuss how to apply these approaches to find global duplicate ads and confront spammers.\n\nPresentation Slides: http://pydatayerevan.aua.am/files/2022/09/Diyar-Mohammadi.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2154, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "http://pydatayerevan.aua.am/files/2022/09/Diyar-Mohammadi.pdf", + "url": "http://pydatayerevan.aua.am/files/2022/09/Diyar-Mohammadi.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/CcsqasfszjE/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXyhlMA8=&rs=AOn4CLDH6JV4EXNd3K7mphqkfbcD-H5l1A", + "title": "Diyar Mohammadi - Near-Duplicate Ad Detection in Online Classified Ad Services | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=CcsqasfszjE" + } + ] +} diff --git a/pydata-yerevan-2022/videos/dmitry-korobchenko-pytorch-geometric-for-graph-neural-nets-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/dmitry-korobchenko-pytorch-geometric-for-graph-neural-nets-pydata-yerevan-2022.json new file mode 100644 index 000000000..c398d30e0 --- /dev/null +++ b/pydata-yerevan-2022/videos/dmitry-korobchenko-pytorch-geometric-for-graph-neural-nets-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Dmitry Korobchenko Presents:\n\nPyTorch Geometric for Graph Neural Nets\n\nIn contrast to classical Deep Learning models (such as MLP, CNN, RNN, Transformers), which are usually applied to tensors and sequences, Graph Neural Net (GNN) is a special type of Deep Learning model which works with non-euclidian data structures, such as graphs. Examples of graph analysis tasks where a data-driven approach can help may include 3D mesh processing, molecular analysis, social graphs data mining, and potentially any other task where traditional DL methods are inapplicable.\n\nPyTorch is an industry-standard Deep Learning framework which provides a lot of useful DL operations and utilities. PyTorch Geometric is a library built on top of PyTorch, implementing a set of tools to create and train Graph Neural Networks.\n\nIn this talk, I will give a very quick and high-level introduction to GNNs and PyTorch Geometrics.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Dmitry-Korobchenko.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2816, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Dmitry-Korobchenko.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Dmitry-Korobchenko.pptx" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/knmPoaqCoyw/maxresdefault.jpg", + "title": "Dmitry Korobchenko - PyTorch Geometric for Graph Neural Nets | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=knmPoaqCoyw" + } + ] +} diff --git a/pydata-yerevan-2022/videos/dmitry-mezhensky-ml-platform-for-insurance-conglomerate-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/dmitry-mezhensky-ml-platform-for-insurance-conglomerate-pydata-yerevan-2022.json new file mode 100644 index 000000000..feda2faa7 --- /dev/null +++ b/pydata-yerevan-2022/videos/dmitry-mezhensky-ml-platform-for-insurance-conglomerate-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Dmitry Mezhensky Presents:\n\nML Platform for Insurance Conglomerate\n\nThe guide through modern ML platform development for the insurance sector and challenges around.\n\nThe talk will be focused on a descriptive guide on how Grid Dynamics was building an ML platform for one of the major US insurance companies, challenges that we have faced, and business benefits clients gained with the new cloud platform.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Dmitry-Mezhensky.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1817, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Dmitry-Mezhensky.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Dmitry-Mezhensky.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/Nht_r5UH8u4/maxresdefault.jpg", + "title": "Dmitry Mezhensky - ML Platform for Insurance Conglomerate | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Nht_r5UH8u4" + } + ] +} diff --git a/pydata-yerevan-2022/videos/elina-israyelyan-ai-powered-solutions-for-cybersecurity-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/elina-israyelyan-ai-powered-solutions-for-cybersecurity-pydata-yerevan-2022.json new file mode 100644 index 000000000..f3ddfd645 --- /dev/null +++ b/pydata-yerevan-2022/videos/elina-israyelyan-ai-powered-solutions-for-cybersecurity-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Elina Israyelyan Presents:\n\nAI-Powered Solutions for Cybersecurity\n\nCyberattacks are continuously growing in volume and entanglement. They target organizations' systems, networks, and private data, causing financial loss, customer loss, and data leakage. As technology improves nowadays, Artificial Intelligence (AI) based solutions help boost Cybersecurity. This talk will discover how AI-powered algorithms are used to stay ahead of Cyberattacks such as Phishing, Lookalike domains, or Name Spoofing.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Elina-Israyelyan.pptx.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1829, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Elina-Israyelyan.pptx.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Elina-Israyelyan.pptx.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/ZU24BFaUAHg/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEMgXihlMA8=&rs=AOn4CLB_DcZPP4WEKNNT7sZSy7Fwu5nC1g", + "title": "Elina Israyelyan - AI-Powered Solutions for Cybersecurity | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=ZU24BFaUAHg" + } + ] +} diff --git a/pydata-yerevan-2022/videos/erik-harutyunyan-active-learning-for-3d-mesh-semantic-segmentation-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/erik-harutyunyan-active-learning-for-3d-mesh-semantic-segmentation-pydata-yerevan-2022.json new file mode 100644 index 000000000..64f280ad7 --- /dev/null +++ b/pydata-yerevan-2022/videos/erik-harutyunyan-active-learning-for-3d-mesh-semantic-segmentation-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Erik Harutyunyan Presents:\n\nActive Learning for 3D Mesh Semantic Segmentation \n\nThe talk is about applications of active learning methods, mainly Monte-Carlo Dropout on 3D mesh/pointcloud semantic segmentation task. The topic is particularly interesting for practical applications of Deep Learning models on this type of data, as it gives a working approach for reducing the amount of data needed for training.\n\nI will briefly go over the 3D mesh/pointcloud semantic segmentation task, and active learning, so that it's clear for the audience not familiar with these concepts. Then I will present the PointNet++ model architecture and Monte-Carlo Dropout approach, that are specifically used in the experiments. And finally, I will share the experiment results with the audience. \n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Dmitry-Korobchenko.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2289, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Dmitry-Korobchenko.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Dmitry-Korobchenko.pptx" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/vDmpP_JRSBY/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEUgYChlMA8=&rs=AOn4CLCpRO0VMQIIe5qJG4bAX751Q-Tzrw", + "title": "Erik Harutyunyan - Active Learning for 3D Mesh Semantic Segmentation | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=vDmpP_JRSBY" + } + ] +} diff --git a/pydata-yerevan-2022/videos/fritz-obermeyer-probabilistic-programming-and-readable-models-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/fritz-obermeyer-probabilistic-programming-and-readable-models-pydata-yerevan-2022.json new file mode 100644 index 000000000..83148fda4 --- /dev/null +++ b/pydata-yerevan-2022/videos/fritz-obermeyer-probabilistic-programming-and-readable-models-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Fritz Obermeyer Presents:\n\nProbabilistic Programming and Readable Models\n\nCode can do many things, and one of those things is to communicate an idea from a writer to a human reader. Probabilistic programming is a relatively recent style of writing code that leads to exceptionally readable and interpretable pieces of code that we call models. The magic of probabilistic programming is that your model code doesn't express how you'll process your data, rather it expresses how the world might have created that data, and relies on semiautomated inference algorithms to convert the model description to data processing logic.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Fritz-Obermeyer.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 4008, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Fritz-Obermeyer.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Fritz-Obermeyer.pptx" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/GBLquwA9hYc/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGH8gSigoMA8=&rs=AOn4CLDDs3XVQmQLi1nnHQ0UwVDSi2shrw", + "title": "Fritz Obermeyer - Probabilistic Programming and Readable Models | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=GBLquwA9hYc" + } + ] +} diff --git a/pydata-yerevan-2022/videos/gevorg-soghomonyan-the-structure-and-interpretation-of-ml-metadata-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/gevorg-soghomonyan-the-structure-and-interpretation-of-ml-metadata-pydata-yerevan-2022.json new file mode 100644 index 000000000..fed346929 --- /dev/null +++ b/pydata-yerevan-2022/videos/gevorg-soghomonyan-the-structure-and-interpretation-of-ml-metadata-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Gevorg Soghomonyan Presents:\n\nThe Structure and Interpretation of ML Metadata\n\nThis talk is targeted both for ML researchers and engineers working on the ML infrastructure. The metadata generated at almost every step of the ML pipeline connects and enables the reproducible and explainable ML infrastructure. During this talk, we will go through how and where metadata is generated in ML infrastructure. The types of the metadata. What's the next generation ML infra stack to leverage the metadata and help build reproducibility, explainability, and governance into your ML systems?\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Gevorg-Soghomonyan.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1654, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Gevorg-Soghomonyan.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Gevorg-Soghomonyan.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/ZMAnEBgJidk/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGGQgZShTMA8=&rs=AOn4CLABDydn6hWEaVO0h3me-W5xcyaJiQ", + "title": "Gevorg Soghomonyan - The Structure and Interpretation of ML Metadata | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=ZMAnEBgJidk" + } + ] +} diff --git a/pydata-yerevan-2022/videos/hadi-abdi-khojasteh-large-scale-representation-learning-in-the-wild-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/hadi-abdi-khojasteh-large-scale-representation-learning-in-the-wild-pydata-yerevan-2022.json new file mode 100644 index 000000000..4e8251b08 --- /dev/null +++ b/pydata-yerevan-2022/videos/hadi-abdi-khojasteh-large-scale-representation-learning-in-the-wild-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Hadi Abdi Khojasteh Presents:\n\nLarge Scale Representation Learning In-the-wild\n\nA significant amount of progress is being made today in the field of representation learning. It has been demonstrated that unsupervised techniques can perform as well as, if not better than, fully supervised ones on benchmarks such as image classification, while also demonstrating improvements in label efficiency by multiple orders of magnitude. In this sense, representation learning is now addressing some of the major challenges in deep learning today. It is imperative, however, to understand systematically the nature of the learnt representations and how they relate to the learning objectives.\n\nIn this talk, we will present a comprehensive overview of representation learning from the beginning to the modern models, contextualize these methods, and discuss the pros and cons of current evaluation methods. New era of deep learning methods that could understand simultaneously the different variety of data will introduce by its evolutions.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Representation-Learning-Presentation.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2454, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Representation-Learning-Presentation.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Representation-Learning-Presentation.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/YRJTpk3yfTc/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXyhlMA8=&rs=AOn4CLD-uvtKRUOo5wpHHc48txYzwZdwxg", + "title": "Hadi Abdi Khojasteh - Large Scale Representation Learning In-the-wild | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=YRJTpk3yfTc" + } + ] +} diff --git a/pydata-yerevan-2022/videos/hadi-abdi-khojasteh-sequential-attention-based-neural-machine-translation-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/hadi-abdi-khojasteh-sequential-attention-based-neural-machine-translation-pydata-yerevan-2022.json new file mode 100644 index 000000000..b1929a3b4 --- /dev/null +++ b/pydata-yerevan-2022/videos/hadi-abdi-khojasteh-sequential-attention-based-neural-machine-translation-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Hadi Abdi Khojasteh Presents:\n\nSequential Attention-Based Neural Machine Translation\n\nThe sequence-to-sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This tutorial will introduce you to sequential and attention models by utilizing the neural machine translation (NMT) model implementation from scratch. Several sequence-to-sequence architectures will be presented under the attention models, including basic models, and intuitions under the attention model. We will then implement the model step by step together and see whether we can figure out the kind of model to translate (or transform) sequences of data such as texts and speech.\n\nGithub Page: https://github.com/hkhojasteh\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2983, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/hkhojasteh", + "url": "https://github.com/hkhojasteh" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/RLfXCcZMv6U/maxresdefault.jpg", + "title": "Hadi Abdi Khojasteh - Sequential Attention-Based Neural Machine Translation | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=RLfXCcZMv6U" + } + ] +} diff --git a/pydata-yerevan-2022/videos/hossein-mortazavi-how-to-use-pandas-efficiently-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/hossein-mortazavi-how-to-use-pandas-efficiently-pydata-yerevan-2022.json new file mode 100644 index 000000000..984f778f2 --- /dev/null +++ b/pydata-yerevan-2022/videos/hossein-mortazavi-how-to-use-pandas-efficiently-pydata-yerevan-2022.json @@ -0,0 +1,51 @@ +{ + "description": "Hossein Mortazavi Presents:\n\nHow to use Pandas Efficiently \n\nThis 90-minute tutorial will demonstrate how to use the Pandas package effectively, which means that the audience will understand the package better after viewing the tutorial.\n\nPresentation Slides: http://pydatayerevan.aua.am/files/2022/09/Hossein-Mortazavi.pptx\n\nGithub Repo: https://github.com/h0ssein2011/Pydata_Yerevan_22\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1242, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "http://pydatayerevan.aua.am/files/2022/09/Hossein-Mortazavi.pptx", + "url": "http://pydatayerevan.aua.am/files/2022/09/Hossein-Mortazavi.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://github.com/h0ssein2011/Pydata_Yerevan_22", + "url": "https://github.com/h0ssein2011/Pydata_Yerevan_22" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/IRr3gx71rnk/maxresdefault.jpg", + "title": "Hossein Mortazavi - How to use Pandas Efficiently | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=IRr3gx71rnk" + } + ] +} diff --git a/pydata-yerevan-2022/videos/hovhannes-margaryan-classical-texture-synthesis-and-beyond-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/hovhannes-margaryan-classical-texture-synthesis-and-beyond-pydata-yerevan-2022.json new file mode 100644 index 000000000..7c4dbcccc --- /dev/null +++ b/pydata-yerevan-2022/videos/hovhannes-margaryan-classical-texture-synthesis-and-beyond-pydata-yerevan-2022.json @@ -0,0 +1,43 @@ +{ + "description": "Hovhannes Margaryan Presents:\n\nClassical Texture Synthesis and Beyond \n\nGiven the structural definition of a texture as a special variation in diverse layers of pixels demonstrating reiterating patterns combined with varied randomness in quantity, the purpose of texture synthesis is to generate an expanded vision of the input texture that perceptually resembles the input. The goal of the talk is to provide an overview of classical and neural texture synthesis algorithms. First, two classical non-parametric methods namely Texture Optimization for Example-based Synthesis and Image Quilting for Texture Synthesis and Transfer are covered. Second, two neural methods of texture synthesis are discussed: Texture Synthesis using CNNs and Non-Stationary Texture Synthesis by Adversarial Expansion. Third, the advantages and disadvantages of these four methods are demonstrated. Results of the indicated four approaches and a visual comparison are provided.\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1660, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/vY46iIxzLuY/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXihlMA8=&rs=AOn4CLD6cRzwiVX80u-gkSQaO0MURfpFnw", + "title": "Hovhannes Margaryan - Classical Texture Synthesis and Beyond | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=vY46iIxzLuY" + } + ] +} diff --git a/pydata-yerevan-2022/videos/hrach-asatryan-large-scale-field-delineation-pydara-yerevan-2022.json b/pydata-yerevan-2022/videos/hrach-asatryan-large-scale-field-delineation-pydara-yerevan-2022.json new file mode 100644 index 000000000..778f2bde7 --- /dev/null +++ b/pydata-yerevan-2022/videos/hrach-asatryan-large-scale-field-delineation-pydara-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Hrach Asatryan Presents:\n\nLarge Scale Field Delineation\n\nThe talk is about methods of doing large-scale field delineation from aerial imagery. Given the increasing importance of global food supplies, AI in agriculture has become integral for later development in the field. Given that modern agriculture is field level, a delineation of fields is required to be able to use such methods.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Hrach-Asatryan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2479, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Hrach-Asatryan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Hrach-Asatryan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/ZHdWwbVjqJg/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEUgYShlMA8=&rs=AOn4CLBD_KVrvqDFztJ1CEuB0HHAruDz1Q", + "title": "Hrach Asatryan - Large Scale Field Delineation | PyDara Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=ZHdWwbVjqJg" + } + ] +} diff --git a/pydata-yerevan-2022/videos/karen-javadyan-streamlit-a-faster-way-to-build-and-share-data-apps-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/karen-javadyan-streamlit-a-faster-way-to-build-and-share-data-apps-pydata-yerevan-2022.json new file mode 100644 index 000000000..10329babf --- /dev/null +++ b/pydata-yerevan-2022/videos/karen-javadyan-streamlit-a-faster-way-to-build-and-share-data-apps-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Karen Javadyan Presents:\n\nStreamlit: A Faster Way to Build and Share Data Apps\n\nPoor tooling slows down data science and machine learning projects.\nStreamlit is a fast way to build and share data apps. It is able to turn data scripts into shareable web apps with minimal effort. Let's hear Karen Javadyanl introduce Streamlit: the fastest way to build and share data apps as Python scripts.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Karen-Javadyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2505, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Karen-Javadyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Karen-Javadyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/jDEtOb69A7g/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEAgWyhlMA8=&rs=AOn4CLBKlOFtinK-fYw2Of4mJqVaAMECUA", + "title": "Karen Javadyan - Streamlit: A Faster Way to Build and Share Data Apps | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jDEtOb69A7g" + } + ] +} diff --git a/pydata-yerevan-2022/videos/katherine-munro-eating-humble-py-from-toy-problem-to-real-world-solution-in-predicting-clv.json b/pydata-yerevan-2022/videos/katherine-munro-eating-humble-py-from-toy-problem-to-real-world-solution-in-predicting-clv.json new file mode 100644 index 000000000..f32fe8e44 --- /dev/null +++ b/pydata-yerevan-2022/videos/katherine-munro-eating-humble-py-from-toy-problem-to-real-world-solution-in-predicting-clv.json @@ -0,0 +1,47 @@ +{ + "description": "Katherine Munro Presents: \n\nEating humble Py: From toy problem to real-world solution in predicting Customer Lifetime Value\n\nThis is the story of my team\u2019s journey from play-problem to real-world solution. Learn, as we learned, what is Customer Lifetime Value and why does everyone in retail suddenly want to predict it? Take a tour of common approaches to solving this problem, from machine learning to good old-fashioned spreadsheets. Feel all the practical pains our clients inflicted on us, and discover why CLV prediction is not as easy as Towards Data Science makes it out to be.\n\nWhether you\u2019re an analytics enthusiast, a novice data scientist or an experienced practitioner, and whether you work in retail or not, there\u2019s something in this talk for you: a little bit of machine learning theory, a peek into a new domain of application you may not be familiar with, or the chance to just cringe in sympathy at problems you know only too well.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Katherine-Munro-.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2620, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Katherine-Munro-.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Katherine-Munro-.pptx" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi_webp/i3S_KBz1nqA/maxresdefault.webp", + "title": "Katherine Munro - Eating Humble Py: From Toy Problem to Real-World Solution in Predicting CLV", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=i3S_KBz1nqA" + } + ] +} diff --git a/pydata-yerevan-2022/videos/liana-minasyan-target-based-sentiment-analysis-with-t5-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/liana-minasyan-target-based-sentiment-analysis-with-t5-pydata-yerevan-2022.json new file mode 100644 index 000000000..6153ed727 --- /dev/null +++ b/pydata-yerevan-2022/videos/liana-minasyan-target-based-sentiment-analysis-with-t5-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Liana Minasyan Presents:\n\nTarget-Based Sentiment Analysis with T5\n\nThe classic sentiment analysis analyzes texts, images, emojis, etc to know what other people think of a product, service, company, or event. While sentiment analysis can be considered one of the accomplished tasks of Natural Language Processing tasks, more fine-grained types of it like Target Based Sentiment Analysis(TSA) or Aspect-based sentiment analysis(ABSA) are not quite the same. In TSA we want to see the sentiment of a given text towards a particular entity(in my case person or organization). This task is one of the non-solved ones. With the T5 question answering transformer model it was possible to solve the task with results 20% higher than the current leaderboards.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Liana-Minasyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1406, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Liana-Minasyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Liana-Minasyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/PYLgpw4w8EU/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGGUgWShVMA8=&rs=AOn4CLAkfuDGsC8Z6-ho7T0YZBrQ6a1ZsA", + "title": "Liana Minasyan - Target-Based Sentiment Analysis with T5 | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=PYLgpw4w8EU" + } + ] +} diff --git a/pydata-yerevan-2022/videos/luka-chkhetiani-scaling-semi-supervised-production-grade-asr-on-200-languages.json b/pydata-yerevan-2022/videos/luka-chkhetiani-scaling-semi-supervised-production-grade-asr-on-200-languages.json new file mode 100644 index 000000000..f8e7d3855 --- /dev/null +++ b/pydata-yerevan-2022/videos/luka-chkhetiani-scaling-semi-supervised-production-grade-asr-on-200-languages.json @@ -0,0 +1,47 @@ +{ + "description": "Luka Chkhetiani Presents:\n\nScaling Semi-Supervised Production-Grade ASR on 200 Languages\n\nSelf-Supervised pretraining has been wildly successful lately, covering almost every domain: Speech, NLP, Vision. Networks, such as: Wav2Vec2, Hubert, JUST, and alikes have enabled rapid development of Speech-related products. In this talk, we're going to go through the end-to-end research and engineering process of production-grade self-supervised ASR in the multilingual setting. Covered topics include: Compute, Data, Scalability, Engineering for Pretraining, and Downstream Tuning.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Luka-Chkhetiani.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2610, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Luka-Chkhetiani.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Luka-Chkhetiani.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/G0uKPBbTOKc/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEggYChlMA8=&rs=AOn4CLCzNwayeevr4HWYoTKKEeKpVQCYig", + "title": "Luka Chkhetiani - Scaling Semi-Supervised Production-Grade ASR on 200 Languages", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=G0uKPBbTOKc" + } + ] +} diff --git a/pydata-yerevan-2022/videos/maria-sahakyan-explainable-ai-as-a-conventional-data-analysis-tool-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/maria-sahakyan-explainable-ai-as-a-conventional-data-analysis-tool-pydata-yerevan-2022.json new file mode 100644 index 000000000..4dd6a6f0c --- /dev/null +++ b/pydata-yerevan-2022/videos/maria-sahakyan-explainable-ai-as-a-conventional-data-analysis-tool-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Maria Sahakyan Presents: \n\nExplainable AI as a Conventional Data Analysis Tool\n\nThe recent surge of interest in Machine Learning (ML) and Artificial Intelligence (AI) has spurred a wide array of models designed to make decisions in a variety of domains, including healthcare [1, 2, 3], financial systems [4, 5, 6, 7], and criminal justice [8, 9, 10], just to name a few. When evaluating alternative models, it may seem natural to prefer those that are more accurate. However, the obsession with accuracy has led to unintended consequences, as developers often strove to achieve greater accuracy at the expense of interpretability by making their models increasingly complicated and harder to understand [11]. This lack of interpretability becomes a serious concern when the model is entrusted with the power to make critical decisions that affect people\u2019s well-being. These concerns have been manifested by the European Union\u2019s recent General Data Protection Regulation, which guarantees a right to explanation, i.e., a right to understand the rationale behind an algorithmic decision that affects individuals negatively [12]. To address these issues, a number of techniques have been proposed to make the decision-making process of AI more understandable to humans. These \u201cExplainable AI \u201d techniques (commonly abbreviated as XAI) are the primary focus of this talk. \n\nThe talk will be divided into three sections, during which the audience will learn:\n\n(i) the differences between existing XAI techniques, \n(ii) the practical implementation of some well-known XAI techniques, and (iii) possible uses of XAI as a conventional data analysis tool.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Maria-Sahakyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2330, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Maria-Sahakyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Maria-Sahakyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/KrS5Ey7TxWs/maxresdefault.jpg", + "title": "Maria Sahakyan - Explainable AI as a Conventional Data Analysis Tool | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=KrS5Ey7TxWs" + } + ] +} diff --git a/pydata-yerevan-2022/videos/marine-palyan-moving-inference-to-triton-servers-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/marine-palyan-moving-inference-to-triton-servers-pydata-yerevan-2022.json new file mode 100644 index 000000000..273a4e8ad --- /dev/null +++ b/pydata-yerevan-2022/videos/marine-palyan-moving-inference-to-triton-servers-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Marine Palyan Presents:\n\nMoving Inference to Triton Servers\n\nThe talk will introduce the audience to Triton Inference Server, the requirements for migrating from regular AWS instances, advantages and benchmarks of our production. \n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Marine-Palyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1474, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Marine-Palyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Marine-Palyan.pptx" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/pqlp1sphDvU/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEcgYShlMA8=&rs=AOn4CLB4O6wyhRmRWK-tvO-PgTb9AIITZA", + "title": "Marine Palyan - Moving Inference to Triton Servers | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=pqlp1sphDvU" + } + ] +} diff --git a/pydata-yerevan-2022/videos/mark-hamazaspyan-using-few-shot-object-detection-for-utility-pole-detection-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/mark-hamazaspyan-using-few-shot-object-detection-for-utility-pole-detection-pydata-yerevan-2022.json new file mode 100644 index 000000000..4d85e3895 --- /dev/null +++ b/pydata-yerevan-2022/videos/mark-hamazaspyan-using-few-shot-object-detection-for-utility-pole-detection-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Mark Hamazaspyan Presents:\n\nUsing Few Shot Object Detection for Utility Pole Detection from Google Street View images\n\nTraditional methods of detecting and mapping utility poles are manual, time-consuming, and costly processes. Current solutions focus on detection of T-shaped (cross-arm-shaped poles) and the lack of labeled data makes it difficult to generalize the process of other types of poles. This work aims to use Few Shot Object Detection techniques to overcome the unavailability of the data and to create a general pole detection model with few labeled images.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Mark-Hamazaspyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1592, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Mark-Hamazaspyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Mark-Hamazaspyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/SBGVRUYutfI/maxresdefault.jpg", + "title": "Mark Hamazaspyan - Using Few Shot Object Detection for Utility Pole Detection | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=SBGVRUYutfI" + } + ] +} diff --git a/pydata-yerevan-2022/videos/meirav-ben-izhak-networkx-your-unexpected-assistant-for-clustering-analysis.json b/pydata-yerevan-2022/videos/meirav-ben-izhak-networkx-your-unexpected-assistant-for-clustering-analysis.json new file mode 100644 index 000000000..8977babc5 --- /dev/null +++ b/pydata-yerevan-2022/videos/meirav-ben-izhak-networkx-your-unexpected-assistant-for-clustering-analysis.json @@ -0,0 +1,47 @@ +{ + "description": "Meirav Ben Izhak Presents:\n\nNetworkX - your Unexpected Assistant for Clustering Analysis \n\nClustering analysis is a common task in data science but it can sometimes get tedious. In this talk, I will present how functionality from the package NetworkX can assist us in analyzing and presenting the results of clustering analysis. This talk assumes no previous knowledge, a brief reminder of graph basics will be given and networkx will be shortly presented. \n\nJoin in if you:\n- want to hear about a new suggested usage of a known data structure, \n- like to get things done more efficiently when you cluster, \n- never heard of networkx but would like to,\n- all of the above.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Meirav-Ben-Izhak.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1440, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Meirav-Ben-Izhak.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Meirav-Ben-Izhak.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/FYwGeQ353DY/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEIgXyhlMA8=&rs=AOn4CLBgQBgx72nfsLvG4E7XiqULd0pPfQ", + "title": "Meirav Ben Izhak - NetworkX - your Unexpected Assistant for Clustering Analysis", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=FYwGeQ353DY" + } + ] +} diff --git a/pydata-yerevan-2022/videos/mher-khachatryan-best-practices-for-coding-in-ml-ds-techniques-to-construct-your-project.json b/pydata-yerevan-2022/videos/mher-khachatryan-best-practices-for-coding-in-ml-ds-techniques-to-construct-your-project.json new file mode 100644 index 000000000..8d1a7332d --- /dev/null +++ b/pydata-yerevan-2022/videos/mher-khachatryan-best-practices-for-coding-in-ml-ds-techniques-to-construct-your-project.json @@ -0,0 +1,47 @@ +{ + "description": "Mher Khachatryan Presents:\n\nBest Practices for Coding in ML/DS - Techniques to Construct your Project \n\nMany engineers, particularly those in Data Science, do not focus on writing better code, which their coworkers will love. This is bad!\n\nWriting cleaner code, and using appropriate tools for experiment logging reduces the time of debugging and the effort spent on the project in the long term. Consequently, the code becomes readable and onboarding new engineers on the project becomes easier.\n\nBy the end of the lecture, attendees will have learned about the importance of having a clean code in the ML project. They will have developed intuition about wiring readable and understandable code and will have acquired knowledge about the general design of a good codebase, and some tools that will help engineers log experiments for a cleaner environment.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Mher-Khachatryan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2232, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Mher-Khachatryan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Mher-Khachatryan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/vc2Bh9i3bNk/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEMgXyhlMA8=&rs=AOn4CLDWRs33gAFZwgJZkrjRQpUlWo-CwQ", + "title": "Mher Khachatryan - Best Practices for Coding in ML/DS - Techniques to Construct your Project", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=vc2Bh9i3bNk" + } + ] +} diff --git a/pydata-yerevan-2022/videos/nacho-aranguren-modern-data-stack-optimising-and-scaling-data-in-a-tech-company.json b/pydata-yerevan-2022/videos/nacho-aranguren-modern-data-stack-optimising-and-scaling-data-in-a-tech-company.json new file mode 100644 index 000000000..4b3332e95 --- /dev/null +++ b/pydata-yerevan-2022/videos/nacho-aranguren-modern-data-stack-optimising-and-scaling-data-in-a-tech-company.json @@ -0,0 +1,47 @@ +{ + "description": "Nacho Aranguren Presents:\n\nModern Data Stack: Optimising and Scaling Data in a Tech Company\n\nThis talk is about a new approach to data integration that DataOps is enabling in tech companies (with Sololearn practical example) to save engineering time, allowing engineers and analysts to pursue higher-value activities, explaining why every tech organisation should have a Data + Analytics Engineering (DataOps) department.\n\nIt answers two main questions: Why should we care about the adoption of data + analytics engineering? What are the steps and processes to start the journey to full adoption?\n\nThis talk will look at themes around that journey: metadata, tools, & organisation action points to paint a picture of what the next phase of the journey looks like. We will go through the modern data stack. We will talk about its architecture, which tools fit where, and how to organise teams to support it. We\u2019ll also touch on the challenges in operationalising warehouses and discuss future technology advancements that could unlock the warehouse to become the platform for business needs and operations.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Nacho-Aranguren.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2471, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Nacho-Aranguren.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Nacho-Aranguren.pptx" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/2ZTvRY-0_og/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEQgXihlMA8=&rs=AOn4CLDjWFvwWfjeajHGGeWNHO4RwHqVFQ", + "title": "Nacho Aranguren - Modern Data Stack: Optimising and Scaling Data in a Tech Company", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=2ZTvRY-0_og" + } + ] +} diff --git a/pydata-yerevan-2022/videos/nura-kawa-the-explainability-problem-towards-understanding-ai-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/nura-kawa-the-explainability-problem-towards-understanding-ai-pydata-yerevan-2022.json new file mode 100644 index 000000000..cd6364835 --- /dev/null +++ b/pydata-yerevan-2022/videos/nura-kawa-the-explainability-problem-towards-understanding-ai-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Nura Kawa Presents:\n\nThe Explainability Problem: Towards Understanding Artificial Intelligence \n\nThis talk discusses Explainable AI using examples of interest for both machine learning practitioners and non-technical audiences. This talk is not very technical; it does not focus on how to apply an existing method to their model. Rather, the talk discusses the problem of Explainability_ as a whole, namely: what is the Explainability Problem and why it must be solved, how recent academic literature addresses the problem, and how the problem will evolve with new legislation.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/The-Explainability-Problem_-Towards-Understanding-Artificial-Intelligence.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2430, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/The-Explainability-Problem_-Towards-Understanding-Artificial-Intelligence.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/The-Explainability-Problem_-Towards-Understanding-Artificial-Intelligence.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/l-YJm6Umz2s/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEggYChlMA8=&rs=AOn4CLBOsx1btVRLnT6NpPx0wg77qnKtug", + "title": "Nura Kawa - The Explainability Problem: Towards Understanding AI | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=l-YJm6Umz2s" + } + ] +} diff --git a/pydata-yerevan-2022/videos/ricardas-ralys-using-python-for-the-pcr-design-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/ricardas-ralys-using-python-for-the-pcr-design-pydata-yerevan-2022.json new file mode 100644 index 000000000..5c7e77453 --- /dev/null +++ b/pydata-yerevan-2022/videos/ricardas-ralys-using-python-for-the-pcr-design-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Ricardas Ralys Presents:\n\nUsing Python for the PCR Design or an Easy Way to Data Analysis in Life Science Projects\n\nThe development of life science projects is often based on Python, as these projects have to do with data analysis and optimization problems -- the language is widespread, universal, and expressive.\nIn one of our projects, we worked on a solution for a widely spread reaction used in health care -- PCR and, consequently, data analysis.\nThis can be easily realized using Python libraries like NumPy. The engineers used NumPy to build graphs based on the sequences\u2019 statistics efficiently.\n\nDuring my talk, I\u2019ll share the benefits of using Python we got, why we need the Steiner tree problem, and why optimizing graphs is the key to solving the optimization problem we had at hand.\n\nQuantori works at the intersection of IT, biology, chemistry, medicine, and other fields of studies to create solutions that maintain and improve people\u2019s health and life quality. So, it is essential to find the optimal algorithms and solutions to complex projects, work out efficient processes, and ensure the products\u2019 high standards.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Ricardas-Ralys.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1404, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Ricardas-Ralys.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Ricardas-Ralys.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/t6vqsvYoj0c/maxresdefault.jpg", + "title": "Ricardas Ralys- Using Python for the PCR Design | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=t6vqsvYoj0c" + } + ] +} diff --git a/pydata-yerevan-2022/videos/rudolf-eremyan-building-data-pipelines-on-aws-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/rudolf-eremyan-building-data-pipelines-on-aws-pydata-yerevan-2022.json new file mode 100644 index 000000000..c4364d497 --- /dev/null +++ b/pydata-yerevan-2022/videos/rudolf-eremyan-building-data-pipelines-on-aws-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Rudolf Eremyan Presents:\n\nBuilding Data Pipelines on AWS\n\nBuilding Data Pipelines on AWS and hidden costs that can destroy budget\u2024\nData is the new fuel. The majority of modern IT companies make their decision based on collected data. An important role during this process plays the data engineering side which is responsible for delivering data in the needed format. During the speech, I want to talk about ways to create Data Pipelines on Amazon Web Services. Except for data engineering, I want to focus attention on the hidden costs that can easily destroy project's budget.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Rudolf-Eremyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2256, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Rudolf-Eremyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Rudolf-Eremyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/JCBLhIiCWW0/maxresdefault.jpg", + "title": "Rudolf Eremyan - Building Data Pipelines on AWS | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=JCBLhIiCWW0" + } + ] +} diff --git a/pydata-yerevan-2022/videos/sergey-hayrapetyan-bachelor-theses-in-deep-learning-submitted-to-an-armenian-university.json b/pydata-yerevan-2022/videos/sergey-hayrapetyan-bachelor-theses-in-deep-learning-submitted-to-an-armenian-university.json new file mode 100644 index 000000000..e1b5478f6 --- /dev/null +++ b/pydata-yerevan-2022/videos/sergey-hayrapetyan-bachelor-theses-in-deep-learning-submitted-to-an-armenian-university.json @@ -0,0 +1,47 @@ +{ + "description": "Sergey Hayrapetyan Presents:\n\nBachelor theses in Deep Learning: Submitted to an Armenian University\n\nBachelor theses written in the area of Deep Learning based object detection will be presented. The main focus is on the detection of vehicles captured from the top, e.g. parking lots, satellites: I will present the challenges we encountered and solved in the scope of Bachelor thesis. The goal of this talk is not only to present the results of young undergraduate students but also to encourage new ones to get involved in the sphere.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Sergey-Hayrapetyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1695, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Sergey-Hayrapetyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Sergey-Hayrapetyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/BVMRlxIbk1E/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXyhlMA8=&rs=AOn4CLBjlqunHlHMrX5M6_bWfWT-Cb6X_g", + "title": "Sergey Hayrapetyan - Bachelor theses in Deep Learning: Submitted to an Armenian University", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=BVMRlxIbk1E" + } + ] +} diff --git a/pydata-yerevan-2022/videos/sona-hambaryan-a-b-testing-in-production-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/sona-hambaryan-a-b-testing-in-production-pydata-yerevan-2022.json new file mode 100644 index 000000000..58e9fbac2 --- /dev/null +++ b/pydata-yerevan-2022/videos/sona-hambaryan-a-b-testing-in-production-pydata-yerevan-2022.json @@ -0,0 +1,43 @@ +{ + "description": "Sona Hambaryan Presents:\n\nA/B Testing in Production\n\nBeing part of statistical learning apparatus, and having a strong mathematical background AB testing remains one of the aspects in the field that continue to be violated and misinterpreted. A big part of violations covers the wrong experiment setup, which I'll try to cover in practice taking into consideration the business setup: whether it's a B2B platform or B2C.\n\nThe main takeaway from this talk will be to understand the pitfalls that relay under experiment setup, where a single disregarded use case can violate the whole experiment outcome. \n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2148, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/kVmfHuMwbA0/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXyhlMA8=&rs=AOn4CLB4csz7mnfjpvMYD5USp3_oO6-feA", + "title": "Sona Hambaryan - A/B Testing in Production | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=kVmfHuMwbA0" + } + ] +} diff --git a/pydata-yerevan-2022/videos/sona-hunanyan-empirical-determinacy-of-posterior-location-and-scale-in-bayesian-hierarchical-models.json b/pydata-yerevan-2022/videos/sona-hunanyan-empirical-determinacy-of-posterior-location-and-scale-in-bayesian-hierarchical-models.json new file mode 100644 index 000000000..2cd93863a --- /dev/null +++ b/pydata-yerevan-2022/videos/sona-hunanyan-empirical-determinacy-of-posterior-location-and-scale-in-bayesian-hierarchical-models.json @@ -0,0 +1,47 @@ +{ + "description": "Sona Hunanyan Presents:\n\nEmpirical Determinacy of Posterior Location and Scale in Bayesian Hierarchical Models\n\nThe parameters in a statistical model are not always identified by the data. In Bayesian analysis, this problem remains unnoticed because of prior assumptions. It is crucial to find out whether the data determine the posterior parameters. In particular, it is important to learn to what extent the spread and the location of the marginal posterior distribution of the parameters are determined by the data.\n\nThe R package ed4bhm allows to investigate the empirical determinacy of marginal posterior parameters, their spread, and location. During this talk, I will showcase the functionality of the package ed4bhm with an application of Bayesian logistic regression to Bacterial resistance data.\n\nIn this talk, you will learn about:\n\n1. The typical problems in a Bayesian Hierarchical Model (BHM)\n2. The theory behind the empirical determinacy of posterior parameters in BHMs\n3. How to fit basic BHM in R\n4. How to apply the R package ed4bhm and how to interpret the results.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Sona-Hunanyan.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2111, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Sona-Hunanyan.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Sona-Hunanyan.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/YoxvKgiSjTE/maxresdefault.jpg", + "title": "Sona Hunanyan- Empirical Determinacy of Posterior Location and Scale in Bayesian Hierarchical Models", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=YoxvKgiSjTE" + } + ] +} diff --git a/pydata-yerevan-2022/videos/tigran-sedrakyan-grovers-quantum-search-for-data-science-and-why-should-we-care.json b/pydata-yerevan-2022/videos/tigran-sedrakyan-grovers-quantum-search-for-data-science-and-why-should-we-care.json new file mode 100644 index 000000000..41effcf58 --- /dev/null +++ b/pydata-yerevan-2022/videos/tigran-sedrakyan-grovers-quantum-search-for-data-science-and-why-should-we-care.json @@ -0,0 +1,47 @@ +{ + "description": "Tigran Sedrakyan Presents:\n\nGrover\u2019s Quantum Search for Data Science and Why should we Care\n\nAmong the most prominent achievements of the quantum computing field is an algorithm known as Grover\u2019s quantum search. This talk focuses on Grover\u2019s algorithm and its applications to machine learning routines. Prior knowledge required is a basic understanding of linear algebra and computer science, and familiarity with the concepts of machine learning.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Tigran-Sedrakyan.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2524, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Tigran-Sedrakyan.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Tigran-Sedrakyan.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/gnymJJRNNvY/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGE0gXyhlMA8=&rs=AOn4CLCPRmnZqYSRevq481CZj6-aSOrMTg", + "title": "Tigran Sedrakyan - Grover\u2019s Quantum Search for Data Science and Why should we Care", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=gnymJJRNNvY" + } + ] +} diff --git a/pydata-yerevan-2022/videos/viacheslav-inozemtsev-building-a-lakehouse-data-platform-using-delta-lake-pyspark-and-trino.json b/pydata-yerevan-2022/videos/viacheslav-inozemtsev-building-a-lakehouse-data-platform-using-delta-lake-pyspark-and-trino.json new file mode 100644 index 000000000..03d8f5458 --- /dev/null +++ b/pydata-yerevan-2022/videos/viacheslav-inozemtsev-building-a-lakehouse-data-platform-using-delta-lake-pyspark-and-trino.json @@ -0,0 +1,47 @@ +{ + "description": "Viacheslav Inozemtsev Presents:\n\nBuilding a Lakehouse data platform using Delta Lake, PySpark, and Trino\n\nIn this talk I would like to present the concept of Lakehouse, which is a novel architecture to resolve problems and combine capabilities of the classical Data Warehouse and Data Lake. I will talk about the Delta Lake table format that resides in the core of Lakehouse. I will demonstrate how Delta Lake integrates with Apache Spark, to build data ingestion pipelines. I will also show how Delta Lake integrates with Apache Trino, to provide a fast SQL-based serving layer. As a result, I will bring all these components together to describe how they enable a modern big data platform. \n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Viacheslav-Inozemtsev.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2533, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Viacheslav-Inozemtsev.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Viacheslav-Inozemtsev.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/FVppO5Pz2Eo/maxresdefault.jpg", + "title": "Viacheslav Inozemtsev - Building a Lakehouse data platform using Delta Lake, PySpark, and Trino", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=FVppO5Pz2Eo" + } + ] +} diff --git a/pydata-yerevan-2022/videos/vladimir-orshulevich-semantic-multimodal-multilingual-similarity-engine-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/vladimir-orshulevich-semantic-multimodal-multilingual-similarity-engine-pydata-yerevan-2022.json new file mode 100644 index 000000000..703910c5c --- /dev/null +++ b/pydata-yerevan-2022/videos/vladimir-orshulevich-semantic-multimodal-multilingual-similarity-engine-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Vladimir Orshulevich Presents:\n\nSemantic Multimodal Multilingual Similarity Engine\n\nSince multimodality became popular, lots of engineers are trying to make a domain-universal search. The search engines that will find in images by textual query, HTML file by piece of audio, and so on. So here is our (Unum) approach with a bias toward GPU accelerating inference (for underlying models) and a passion to distribute everything.\n\nDuring the presentation, we will discuss the following questions:\n\n-How to build fast and precise Semantic Textual Similarity engine. \n-Multilingual sentence encoders. \n-What is CLIP and how can we find an image with a textual query. \n-Building indices: Approximate Nearest Neighbor Algorithms and toolkits. \n-What is the future of the semantic cross-domain search.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Vladimir-Orshulevich.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2430, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Vladimir-Orshulevich.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Vladimir-Orshulevich.pdf" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/hUO0STe6Tls/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXihlMA8=&rs=AOn4CLBAGS9125YPFjMO1Q26kwBMq66iEg", + "title": "Vladimir Orshulevich - Semantic Multimodal Multilingual Similarity Engine | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=hUO0STe6Tls" + } + ] +} diff --git a/pydata-yerevan-2022/videos/wolfgang-weidinger-data-ai-hype-and-fear-recipe-for-disaster-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/wolfgang-weidinger-data-ai-hype-and-fear-recipe-for-disaster-pydata-yerevan-2022.json new file mode 100644 index 000000000..ea1a3ba2b --- /dev/null +++ b/pydata-yerevan-2022/videos/wolfgang-weidinger-data-ai-hype-and-fear-recipe-for-disaster-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Wolfgang Weidinger Presents:\n\nData, AI, Hype and Fear - Recipe for Disaster?\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Wolfgang-Weidinger.pptx\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 3370, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Wolfgang-Weidinger.pptx", + "url": "https://pydatayerevan.aua.am/files/2022/09/Wolfgang-Weidinger.pptx" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/kThC80J5QZs/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEggYShlMA8=&rs=AOn4CLDKn4fuF9j7dhZutyFB87MHTowVDA", + "title": "Wolfgang Weidinger - Data, AI, Hype and Fear - Recipe for Disaster? | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=kThC80J5QZs" + } + ] +} diff --git a/pydata-yerevan-2022/videos/zohreh-jafari-building-a-streaming-e-health-data-pipeline-when-and-how-pydata-yerevan-2022.json b/pydata-yerevan-2022/videos/zohreh-jafari-building-a-streaming-e-health-data-pipeline-when-and-how-pydata-yerevan-2022.json new file mode 100644 index 000000000..ced778340 --- /dev/null +++ b/pydata-yerevan-2022/videos/zohreh-jafari-building-a-streaming-e-health-data-pipeline-when-and-how-pydata-yerevan-2022.json @@ -0,0 +1,47 @@ +{ + "description": "Zohreh Jafari Presents:\n\nBuilding a Streaming (E-health) Data Pipeline: When and How?\n\nIn this talk, we discuss streaming and the real-time data stack as a solution for analyzing massive, unbounded data sets that are increasingly common in many modern businesses in different fields and their need for more timely and accurate answers.\n\nA streaming data pipeline flows data continuously from source to destination as it is generated, making it being processed along the way so they are used when the analytics, application, or business process requires an updating data flow for an on-time analysis. This analysis can be descriptive like a data dashboard, diagnostic like monitoring logs, predictive like an online fraud detection system, or prescriptive like data process in self-driving cars.\n\nDuring the talk, examples of e-health data pipelines plus our experience in setting up a data streaming stack help to explicitly of the subject.\n\nPresentation Slides: https://pydatayerevan.aua.am/files/2022/09/Zohreh-Jafari.pdf\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2528, + "language": "eng", + "recorded": "2022-08-12", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/yerevan2022/" + }, + { + "label": "https://pydatayerevan.aua.am/files/2022/09/Zohreh-Jafari.pdf", + "url": "https://pydatayerevan.aua.am/files/2022/09/Zohreh-Jafari.pdf" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/gbSjgSk_b_Q/hqdefault.jpg?sqp=-oaymwEmCOADEOgC8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGEEgXyhlMA8=&rs=AOn4CLDpRWBwQj1BFr-udfTFpo-qg2osew", + "title": "Zohreh Jafari - Building a Streaming (E-health) Data Pipeline: When and How? | PyData Yerevan 2022", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=gbSjgSk_b_Q" + } + ] +}