GabriellJacinto / Deep-Learning

Exercises and notes from the book Deep Learning

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Book: Deep Learning

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Chapter 1: Introduction
  • Algorithms
    • C++
    • Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 2: Linear Algebra
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 3: Probability abd Information Theory
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 4: Numerical Computation
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 5: Machine Learning Basics
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 6: Deep Feedfoward Networks
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 7: Regularization for Deep Learning
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 8: Optimization for Training Deep Models
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 9: Convolutional Networks
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 10: Sequence Modeling: Recurrent and Recursive Nets
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 11: Practical Methodology
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
  • Notes
  • Exercices
Chapter 12: Applications
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 13: Linear Factor Models
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 14: Autoencoders
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 15: Representation Learning
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 16: Structured Probabilistic Models for Deep Learning
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 17: Monte Carlo Methods
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 18: Confronting the Partition Function
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 19: Approximate Inference
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices
Chapter 20: Deep Generative Models
  • Algorithms
    • C++
    • Python / Mojo
    • Lisp
    • Prolog
  • Notes
  • Exercices

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Exercises and notes from the book Deep Learning