Deep Learning with Python

PART1 : THE FUNDAMENTALS OF DEEP LEARNING

1. WHAT IS DEEP LEARNING?
   • 1.1 Artificial intelligence, machine learning, and deep learning
   • 1.2 Before deep learning: a brief history of machine learning
   • 1.3 Why deep learning? Why now?
2. BEFORE WE START: THE MATHEMATICAL BLOCKS OF NEURAL NETWORKS
   • 2.1 A first look at a neural network
   • 2.2 Data representations for neural networks
   • 2.3 The gears of neural networks: tensor operations
   • 2.4 The engine of neural networks: gradient-based optimization
   • 2.5 Looking back at out first exampel
3. GETTING STARTED WITH NEURAL NETWORKS
   • 3.1 Anatomy of a neural network
   • 3.2 Introduction to Keras
   • 3.3 Setting up a deep-learning workstation
   • 3.4 Classifying movie reviews: a binary classification example
   • 3.5 Classifying newswires: a multiclass classification example
   • 3.6 Predicting house prices: a regression example
4. FUNDAMENTALS OF MACHINE LEARNING
   • 4.1 Four branches of machine learning
   • 4.2 Evaluating machine-learning models
   • 4.3 Data preprocessing, feature engineering, and feature learning
   • 4.4 Overfitting and underfitting
   • 4.5 The universal workflow of machine learning

PART2 : DEEP LEARNING IN PRACTICE

5. DEEP LEARNING FOR COMPUTER VISION
   • 5.1 Introduction to convnets
   • 5.2 Training a convnet from scratch on a small dataset
   • 5.3 Using a pretrained convnet
   • 5.4 Visualizing what convnets learn
6. DEEP LEARNING FOR TEXT AND SEQUENCES
   • 6.1 Working with text data
   • 6.2 Understanding recurrent neural networks
   • 6.3 Advanced use of recurrent neural networks
   • 6.4 Sequence processing with convnets
7. ADVANCED DEEP LEARNING BEST PRACTICES
   • 7.1 Going beyond the Sequenctial model: the Keras functional API
   • 7.2 Inspecting and monitoring deep-learning models using Keras callbacks and TensorBoard
   • 7.3 Getting the most out of your models
8. GENERATIVE DEEP LEARNING
   • 8.1 Generative deep learning
   • 8.2 DeepDream
   • 8.3 Neural style transfer
   • 8.4 Generating images with variational autoencoders
   • 8.5 Introduction to generative adversarial networks
9. CONCLUSIONS