Welcome to ML Compiled!

Quick definitions and intuitive explanations around machine learning.

This is a work in progress and is being continually updated.

Contents

• About
• How to contribute
• Notation

Maths

• Calculus
• Information theory
• Geometry
• Linear algebra
• Monte Carlo methods
• Probability
• Statistics

Neural networks

• Activation functions
• Convolutional networks
• Embeddings
• Generative networks
• Initialization
• Layers
• Loss functions
• Optimization
• Recurrent networks
• Regularization
• Sequence to sequence

Other models

• Decision Trees
• Ensemble models
• Gaussian processes
• Probabilistic graphical models
• Regression
• SVMs

Problems and tasks

• Adversarial examples
• Computer vision
• Density estimation
• Evaluation metrics
• Hyperparameter optimization
• Modelling uncertainty
• Multimodal learning
• Natural language processing (NLP)
• Training with limited data

Reinforcement learning

• Applications
• Basics
• Explore-exploit dilemma
• Search
• Temporal-difference learning
• Types of policy-learning algorithms