Introduction
Probability Basics
Continuous Probability Distributions
Bayesian ML
Sampling from Distributions
Graphical Models
Mixture Models
Information Theory
Variational Models
Bayesian ML with PyMC
Bayesian ML with Pyro
Bayesian ML with PyTorch
Bayesian ML with Tensorflow Probability
Bayesian ML with Julia
Gaussian Processes
Linear Regression
Generative Adversarial Networks
Hidden Markov Models
Tensor Factorization
Neural Networks
Expectation Maximization
Recommender Systems
Active Learning
JAX
Appendix 1 - Linear Algebra for ML
Appendix 2 - Stochastic processes
References
This book is an effort to programmatically explain complex ML concepts with interactive codes and visualizations.
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MLE, MAP and Fully Bayesian (conjugate prior and MCMC) for coin toss
