Deep Learning

Interactive tutorials based on the comprehensive textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Master the mathematical foundations and practical applications of deep learning through structured learning paths

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Part I: Applied Math and Machine Learning Basics

Build the mathematical foundation necessary for understanding deep learning

1
ML DL Foundations
Introduction

Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn and make decisions from data, much like how the human brain processes information.

Study Chapter 60 min read
2
Mathematics Linear Algebra Foundations
Linear Algebra

Essential linear algebra concepts including vectors, matrices, eigendecomposition, and singular value decomposition for deep learning.

Study Chapter 60 min read
3
Probability Information Theory Statistics
Probability and Information Theory

Probability theory fundamentals, Bayes' rule, information theory, and key concepts for uncertainty in machine learning.

Study Chapter 75 min read
4
Numerical Methods Optimization Computation
Numerical Computation

Numerical stability, conditioning, gradient-based optimization, and computational considerations for machine learning algorithms.

Study Chapter 50 min read
5
Machine Learning Supervised Learning Fundamentals
Machine Learning Basics

Core machine learning concepts, capacity, overfitting, underfitting, hyperparameters, and estimators.

Study Chapter 90 min read

Part II: Modern Practical Deep Networks

Learn the practical techniques and architectures that power modern deep learning systems

6
Neural Networks Feedforward MLP
Deep Feedforward Networks

Multilayer perceptrons, activation functions, architecture design, and the XOR problem solution.

Study Chapter 80 min read
7
Regularization Dropout Overfitting
Regularization for Deep Learning

Techniques to improve generalization: L1/L2 regularization, dropout, early stopping, and data augmentation.

Study Chapter 70 min read
8
Optimization SGD Adam
Optimization for Training Deep Models

Gradient descent variants, adaptive learning rates, second-order methods, and optimization challenges.

Study Chapter 85 min read
9
CNN Computer Vision Convolution
Convolutional Networks

Convolutional operations, pooling, CNN architectures, and applications in computer vision.

Study Chapter 95 min read
10
RNN LSTM Sequences
Sequence Modeling: RNNs

Recurrent neural networks, LSTM, GRU, bidirectional RNNs, and sequence modeling techniques.

Study Chapter 100 min read
11
Methodology Best Practices Debugging
Practical Methodology

Performance metrics, debugging strategies, hyperparameter optimization, and practical guidelines for deep learning projects.

Study Chapter 65 min read
12
Applications Computer Vision NLP
Applications

Real-world applications of deep learning in computer vision, natural language processing, and other domains.

Study Chapter 75 min read

Part III: Deep Learning Research

Advanced topics and cutting-edge research directions in deep learning

14
Autoencoders Unsupervised Dimensionality
Autoencoders

Undercomplete and overcomplete autoencoders, regularized autoencoders, and representation learning.

Study Chapter 70 min read
15
Representation Learning Features
Representation Learning

Learning representations, greedy layer-wise pretraining, and transfer learning strategies.

Study Chapter 80 min read
20
Generative VAE GAN
Deep Generative Models

Variational autoencoders, generative adversarial networks, and other approaches to generative modeling.

Study Chapter 90 min read
View Complete Book Online

Free online version available at deeplearningbook.org

Key Topics Covered

Comprehensive coverage of mathematical foundations and practical deep learning techniques

Linear Algebra Probability Theory Information Theory Numerical Computation Machine Learning Neural Networks Deep Feedforward Networks Regularization Optimization Convolutional Networks Recurrent Networks Practical Methodology Autoencoders Representation Learning Generative Models Monte Carlo Methods Variational Inference

Authors

Written by leading experts in the field of machine learning and deep learning

Ian Goodfellow

Research Scientist
Creator of GANs

Yoshua Bengio

Professor, University of Montreal
Turing Award Winner

Aaron Courville

Professor, University of Montreal
Co-founder, Element AI