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Code-First-ML

Code-First-ML

Introduction

MLE, MAP and Fully Bayesian (conjugate prior and MCMC) for coin toss
Variational Inference from scratch in JAX

Probability - Univariate Models

Sample Space and Random Variables
Properties of RV
Derivations for moments of univariate normal distribution

Probability - Multivariate Models

Multivariate Normal Distribution: Introduction
Multivariate Normal Distribution: Marginals

Statistics

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

Decision Theory

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

Information Theory

Drawing graphical models

Linear Algebra

GMM learning

Optimization

Understanding KL-Divergence

Neural networks for structured data

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

Recommender systems

Recommender Systems in Keras
Neural Networks for Collaborative Filtering

References

References

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