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Machine Learning Handbook - Home
  • Machine Learning Katas

overview

  • Introduction to Machine Learning
  • Machine Learning in action
  • Introduction to Reinforcement Learning

Fundamentals

  • Handling data
  • Assessing results
  • Training models

Algorithms

  • Classic Machine Learning
    • K-Nearest Neighbors
    • Linear Regression
    • Logistic Regression
    • Decision Trees & Random Forests
    • Bayesian Methods
    • Support Vector Machines
    • K-Means
  • Neural Networks and Deep Learning
    • Artificial Neural Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks
    • Autoencoders
    • Neural Style Transfer
    • Generative Adversarial Networks
    • Transformers

Engineering

  • Introduction to MLOps
  • Machine Learning issues

Tools

  • Python
    • The Python ecosystem
    • Python cheatsheet
    • Python good practices
  • NumPy
  • Keras
  • PyTorch

Reference

  • Activation functions
  • Glossary
  • Acknowledgments
  • Repository
  • Open issue

Index

A | B | C | D | P | S

A

  • Accuracy
  • Activation function
  • AUROC
  • Automatic differentiation (autodiff)

B

  • Backpropagation
  • Batch
  • Bias
  • Boosting
  • Broadcasting

C

  • Confusion matrix

D

  • Decision boundary

P

  • Precision

S

  • Self-supervised learning

By Baptiste Pesquet

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