ML Glossary

Basics

  • Linear Regression
    • Introduction
    • Simple regression
      • Making predictions
      • Cost function
      • Gradient descent
      • Training
      • Model evaluation
      • Summary
    • Multivariable regression
      • Growing complexity
      • Normalization
      • Making predictions
      • Initialize weights
      • Cost function
      • Gradient descent
      • Simplifying with matrices
      • Bias term
      • Model evaluation
  • Gradient Descent
    • Introduction
    • Learning rate
    • Cost function
    • Step-by-step
  • Logistic Regression
    • Introduction
      • Comparison to linear regression
      • Types of logistic regression
    • Binary logistic regression
      • Sigmoid activation
      • Decision boundary
      • Making predictions
      • Cost function
      • Gradient descent
      • Mapping probabilities to classes
      • Training
      • Model evaluation
    • Multiclass logistic regression
      • Procedure
      • Softmax activation
      • Scikit-Learn example
  • Glossary

Math

  • Calculus
    • Introduction
    • Derivatives
      • Geometric definition
      • Taking the derivative
      • Step-by-step
      • Machine learning use cases
    • Chain rule
      • How It Works
      • Step-by-step
      • Multiple functions
    • Gradients
      • Partial derivatives
      • Step-by-step
      • Directional derivatives
      • Useful properties
    • Integrals
      • Computing integrals
      • Applications of integration
        • Computing probabilities
        • Expected value
        • Variance
  • Linear Algebra
    • Vectors
      • Notation
      • Vectors in geometry
      • Scalar operations
      • Elementwise operations
      • Dot product
      • Hadamard product
      • Vector fields
    • Matrices
      • Dimensions
      • Scalar operations
      • Elementwise operations
      • Hadamard product
      • Matrix transpose
      • Matrix multiplication
      • Test yourself
    • Numpy
      • Dot product
      • Broadcasting
  • Probability (TODO)
    • Links
    • Screenshots
    • License
  • Statistics (TODO)
  • Notation
    • Algebra
    • Calculus
    • Linear algebra
    • Probability
    • Set theory
    • Statistics

Neural Networks

  • Concepts
    • Neural Network
    • Neuron
    • Synapse
    • Weights
    • Bias
    • Layers
    • Weighted Input
    • Activation Functions
    • Loss Functions
    • Optimization Algorithms
    • Gradient Accumulation
  • Forwardpropagation
    • Simple Network
      • Steps
      • Code
    • Larger Network
      • Architecture
      • Weight Initialization
      • Bias Terms
      • Working with Matrices
      • Dynamic Resizing
      • Refactoring Our Code
      • Final Result
  • Backpropagation
    • Chain rule refresher
    • Applying the chain rule
    • Saving work with memoization
    • Code example
  • Activation Functions
    • Linear
    • ELU
    • ReLU
    • LeakyReLU
    • Sigmoid
    • Tanh
    • Softmax
  • Layers
    • BatchNorm
    • Convolution
    • Dropout
    • Pooling
    • Fully-connected/Linear
    • RNN
    • GRU
    • LSTM
  • Loss Functions
    • Cross-Entropy
    • Hinge
    • Huber
    • Kullback-Leibler
    • RMSE
    • MAE (L1)
    • MSE (L2)
  • Optimizers
    • Adagrad
    • Adadelta
    • Adam
    • Conjugate Gradients
    • BFGS
    • Momentum
    • Nesterov Momentum
    • Newton’s Method
    • RMSProp
    • SGD
  • Regularization
    • Data Augmentation
    • Dropout
    • Early Stopping
    • Ensembling
    • Injecting Noise
    • L1 Regularization
    • L2 Regularization
  • Architectures
    • Autoencoder
    • CNN
    • GAN
    • MLP
    • RNN
    • VAE

Algorithms (TODO)

  • Classification
    • Bayesian
    • Decision Trees
    • K-Nearest Neighbor
    • Logistic Regression
    • Random Forests
    • Boosting
    • Support Vector Machine
  • Clustering
    • Centroid
    • Density
    • Distribution
    • Hierarchical
    • K-Means
    • Mean shift
  • Regression
    • Ordinary Least Squares
    • Polynomial
    • Lasso
    • Ridge
    • Stepwise
  • Reinforcement Learning
    • Note on Terminology
    • Eploration vs. Exploitation
    • MDPs and Tabular methods
    • Monte Carlo methods
    • Temporal-Difference Learning
    • Planning
    • On-Policy vs. Off-Policy Learning
    • Model-Free vs. Model-Based Approaches
    • Imitation Learning
    • Q-Learning
    • Deep Q-Learning
    • Examples of Applications
    • Links

Resources

  • Datasets
  • Libraries
  • Papers
  • Other

Contributing

  • How to contribute
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