ML Compiled
latest
  • About
  • How to contribute
  • Notation

Maths

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

Neural networks

  • Activation functions
  • Convolutional networks
  • Embeddings
  • Generative networks
  • Initialization
  • Layers
  • Loss functions
  • Normalization
  • Optimization
  • Regularization
  • Sequence models

Other models

  • Decision Trees
  • Ensemble models
  • Gaussian processes
  • Graphical models
  • Regression
  • SVMs

Problems and tasks

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

Reinforcement learning

  • Applications
  • Basics
  • Explore-exploit dilemma
  • Search
  • Temporal-difference learning
  • Types of policy-learning algorithms
ML Compiled
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  • Welcome to ML Compiled!
  • Edit on GitHub

Welcome to ML Compiled!¶

Quick definitions and intuitive explanations around machine learning.

Contents¶

  • About
  • How to contribute
  • Notation

Maths

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

Neural networks

  • Activation functions
  • Convolutional networks
  • Embeddings
  • Generative networks
  • Initialization
  • Layers
  • Loss functions
  • Normalization
  • Optimization
  • Regularization
  • Sequence models

Other models

  • Decision Trees
  • Ensemble models
  • Gaussian processes
  • Graphical models
  • Regression
  • SVMs

Problems and tasks

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

Reinforcement learning

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