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Code-First Machine Learning

Introduction

MLE, MAP and Fully Bayesian (conjugate prior and MCMC) for coin toss

Probability

Sample Space
Random Variables
Probability Mass Function (PMF)
Properties of RV
Derivations for moments of univariate normal distribution
Multivariate Normal Distribution: Introduction
Multivariate Normal Distribution: Marginals

Variational Inference

Variational Inference from scratch in JAX

MCMC

Simple rejection sampling
Metropolis Hastings
Importance sampling
Rejection sampling
Sampling from univariate and multivariate normal distributions using Box-Muller transform
Sampling from common distributions
Gibbs sampling
Coin tosses and MCMC

Bayesian ML

Bayesian ML: Fundamentals
Bayesian linear regression
Bayesian model selection
Marginal likelihood
Marginal likelihood for Bayesian linear regression

Graphical Models

Drawing graphical models

Mixture Models

GMM learning

Information Theory

Understanding KL-Divergence

Variational Models

Variational Inference

Bayesian ML with PyMC

Bayesian linear regression
Bayesian logistic regression
Gaussian process regression in PyMC
Local Lengthscale GP with PyMC

Bayesian ML with Pyro

Probabilistic Programming in Pyro
Linear Regression using Pyro
Pyro Conditioning

Bayesian ML with PyTorch

Maximum Likelihood Estimation (MLE) for parameters of univariate and multivariate normal distribution in PyTorch
Maximum A-Posteriori (MAP) for parameters of univariate and multivariate normal distribution in PyTorch
Probabilstic PCA using PyTorch distributions
Logistic Regression using PyTorch distributions

Bayesian ML with Tensorflow Probability

Testing out some distributions in Tensorflow Probability
Coin Toss (MLE, MAP, Fully Bayesian) in TF Probability
Linear Regression in Tensorflow Probability
Linear Regression in TF Probability using JointDistributionCoroutineAutoBatched
Simple Directed Graphical Models in TF Probability

Bayesian ML with Julia

Linear Regression from scratch in Julia

Gaussian Processes

Some experiments in Gaussian Processes Regression
Local Lengthscale GP
Deep Kernel Learning
Automatic relevance determination (ARD)
GP v/s Deep GP on 2d data
Gaussian Processes with Random Fourier Features
Learning Gaussian Process regression parameters using gradient descent
Learning Gaussian Process regression parameters using mini-batch stochastic gradient descent
Understanding Kernels in Gaussian Processes Regression
Deep Kernel Learning
GP Classification

Linear Regression

Coordinate descent failure example

Generative Adversarial Networks

A programming introduction to GANs

Hidden Markov Models

HMM Simulation for Unfair Casino Problem
HMM Simulation for Continuous HMM

Tensor Factorization

Out of matrix non-negative matrix factorisation
Out of Tensor factorisation
Adagrad based matrix factorization
Constrained Non-negative matrix factorisation using CVXPY

Neural Networks

Some Neural Network Classification
Learning neural network for XOR
Neural Networks from scratch
Signal denoising using RNNs in PyTorch

Expectation Maximization

Programatically understanding Expectation Maximization

Recommender Systems

Recommender Systems in Keras
Neural Networks for Collaborative Filtering

Active Learning

Active Learning
Query by Committee
Active Learning with Bayesian Linear Regression
Problem with Expected Model Change

NNs for Time Series

Encoder-Decoder Networks

JAX

Using PRNG key
Autograd in JAX and PyTorch
Programatically understanding Adagrad

Appendix 1 - Linear Algebra for ML

Eigen values
Determinant
Positive definiteness
Matrix as transformation and interpreting low rank matrix

Appendix 2 - Stochastic processes

Stochastic processes
Stationarity of time-series stochastic process
Stationarity of stochastic processes II

References

References

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Code-First Machine Learning

Code-First Machine Learning#

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

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