- Computer Vision Basics in Microsoft Excel
- http://www.gameaipro.com/
- Understanding Machine Learning: From Theory to Algorithms - PDF
- http://www.oranlooney.com/post/ml-from-scratch-part-1-linear-regression/
- https://mml-book.github.io/
- https://ngoldbaum.github.io/posts/python-vs-rust-nn/
- The Matrix Calculus You Need for Deep Learning
- https://www.lesswrong.com/posts/9L9XuXhLYBm47yYkf/a-primer-on-matrix-calculus-part-1-basic-review
- https://www.lesswrong.com/posts/KKwv9kcQz29vqPLAD/a-primer-on-matrix-calculus-part-2-jacobians-and-other-fun
- https://www.lesswrong.com/posts/u4DWp72GyLsQn24oR/a-primer-on-matrix-calculus-part-3-the-chain-rule
- https://news.ycombinator.com/item?id=20674745
- http://www.cis.upenn.edu/~jean/math-basics.pdf
- Population based training of neural networks
- L2 Regularization and Batch Norm
- Meta-learning neural Bloom filters
- http://www.datastuff.tech/machine-learning/why-do-neural-networks-need-an-activation-function/
- fast.ai: Deep Learning from the Foundations
- Logistic Regression from Bayes' Theorem
- GANs World Resources
- MIT 6.S191 Introduction to Deep Learning
- MIT 18.065 - Spring 2018: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
- Machine Learning Course with Python
- Learn_Machine_Learning_in_3_Months
- A Recipe for Training Neural Networks
- A visual proof that neural nets can compute any function
- Kalman and Bayesian Filters in Python
- What is a Manifold
- https://news.ycombinator.com/item?id=19499515
- Illustrated word2vec
- The Illustrated Transformer
- http://bugra.github.io/work/notes/2014-08-23/on-machine-learning/
- Machine Learning - HN
- Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville
- http://datanice.github.io/machine-learning-101-what-is-regularization-interactive.html
- https://smalldata.tech/blog/2016/05/03/building-a-simple-neural-net-in-java
- Neural networks from scratch in Scala
- CNNs, Part 2
- CNNs, Part 1: An Introduction to Convolutional Neural Networks
- Build a Neural Network
- Machine Learning for Beginners: An Introduction to Neural Networks
- https://github.com/eriklindernoren/ML-From-Scratch
- Live Coding Deep Learning Library - Joel Grus
- http://people.idsia.ch/~juergen/who-invented-backpropagation.html
- Backpropogation is Just Steepest Descent with Automatic Differentiation
- More Descent, Less Gradient
- It’s Only Natural: An Excessively Deep Dive Into Natural Gradient Optimization
- Natural Gradient Descent
- An overview of gradient descent optimization algorithms
- A Brief Survey of Deep Reinforcement Learning
- Python Implementation of Reinforcement Learning: An Introduction
- Bayesian Bandits explained simply
- https://www.datahubbs.com/multi-armed-bandits-reinforcement-learning-2/
- https://lilianweng.github.io/lil-log/2018/01/23/the-multi-armed-bandit-problem-and-its-solutions.html
- https://multithreaded.stitchfix.com/blog/2018/11/08/bandits/
- http://ai.berkeley.edu/slides/
- https://youtu.be/qAvY2tkMHHA
- https://towardsdatascience.com/solving-the-multi-armed-bandit-problem-b72de40db97c