Resources.md

Table of Contents

1. Resources
   1. Prerequisites
      1. Python
      2. Jupyter
      3. Github
      4. Numpy and other Libraries
      5. Common Terminologies
      6. Links
   2. Resource Hubs
   3. Machine Learning Intro
   4. Neural Networks
   5. Clustering
   6. What are Genetic Algorithms?
   7. Cool Applications of AI

Resources

Prerequisites

Python

• Corey Schafer Videos
• Automate the boring stuff : For other interesting python projects

Jupyter

• Jupyter Notebook Quickstart

Github

• Github Guides
• Git tower videos
• Git tower ebook

Numpy and other Libraries

• Numpy Quickstart guide
• CS231N Tutorials

Common Terminologies

Learn about the following terminologies:

• Accuracy, Precision and F-Score
• Curse of Dimensionality
• Bias Variance Tradoff
• Loss Functions

Links

• Wikipedia Accuracy vs Precision
• Wikipedia Bias vs Variance
• Wikipedia Curse of Dimensionality

Resource Hubs

• Awesome Deep Learning - Github **

Machine Learning Intro

• Visual Introduction to Machine Learning Part 1
• Visual Introduction to Machine Learning Part 2
• Introduction to Curse of Dimensionality
• Google’s Crash Course

Neural Networks

• Neural Networks as arbitrary function fitters
• 3B1B What is a Neural Network?
• Computational Graphs Explained

Clustering

• Wikipedia Clustering
• 5 Clustering Algorithms

What are Genetic Algorithms?

• UC Davis Introduction to Genetic Algorthms
• Evolutionary computation course (AEC 02 and 03 only)
• Genetic Algorithms simply explained
• David Goldberg’s Book on Genetic Algorithms and Soft Computing

Cool Applications of AI

• Pixrv
• This person does not exist
• Quick Draw
• Google-AI Experiments