Deep Learning with Python

Jul 6, 2026

Upload by Eliana

Deep Learning with Python, by François Chollet, presents a practical, end-to-end introduction to deep learning: from foundational concepts such as neural network representations, tensor operations, and gradient-based optimization, to the core machine-learning workflow and regularization strategies. The book then applies these ideas to real tasks, including computer vision with convolutional networks and modern approaches for text and sequences, covering best practices, generative deep learning, and key conclusions drawn from applied research.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages373

Reading Time15 h 40 min

PART 1 FUNDAMENTALS OF DEEP LEARNING

What is deep learning?

Before we begin: the mathematical building blocks of neural networks

Getting started with neural networks

Fundamentals of machine learning

PART 2 DEEP LEARNING IN PRACTICE

Deep learning for computer vision

Deep learning for text and sequences

Advanced deep-learning best practices

Generative deep learning

Conclusions

Deep Learning with Python FRANÇOIS CHOLLET MANNING SHELTER ISLAND ©2018 by Manning Publications Co. ISBN 9781617294433 Printed in the United States of America brief contents PART 1 FUNDAMENTALS OF DEEP LEARNING .................................. 1 1 ■ What is deep learning? 3 2 ■ Before we begin: the mathematical building blocks of neural networks 25 3 ■ Getting started with neural networks 56 4 ■ Fundamentals of machine learning 93 PART 2 DEEP LEARNING IN PRACTICE ........................................ 117 5 ■ Deep learning for computer vision 119 6 ■ Deep learning for text and sequences 178 7 ■ Advanced deep-learning best practices 233 8 ■ Generative deep learning 269 9 ■ Conclusions 314 contents preface xiii acknowledgments xv about this book xvi about the author xx about the cover xxi PART 1 FUNDAMENTALS OF DEEP LEARNING ................... 1 1 What is deep learning? 3 1.1 Artificial intelligence, machine learning, and deep learning 4 Artificial intelligence 4 ■ Machine learning 4 ■ Learning representations from data 6 ■ The “deep” in deep learning 8 Understanding how deep learning works, in three figures 9 What deep learning has achieved so far 11 ■ Don’t believe the short-term hype 12 ■ The promise of AI 13 1.2 Before deep learning: a brief history of machine learning 14 Probabilistic modeling 14 ■ Early neural networks 14 Kernel methods 15 ■ Decision trees, random forests, and gradient boosting machines 16 ■ Back to neural networks 17 ■ What makes deep learning different 17 The modern machine-learning landscape 18 1.3 Why deep learning? Why now? 20 Hardware 20 ■ Data 21 ■ Algorithms 21 ■ A new wave of investment 22 ■ The democratization of deep learning 23 ■ Will it last? 23 2 Before we begin: the mathematical building blocks of neural networks 25 2. 1 A first look at a neural network 27 2.2 Data representations for neural networks 31 Scalars (0D tensors) 31 ■ Vectors (1D tensors) 31 Matrices (2D tensors) 31 ■ 3D tensors and higherdimensional tensors 32 ■ Key attributes 32 Manipulating tensors in Numpy 34 ■ The notion of data batches 34 ■ Real-world examples of data tensors 35 ■ Vector data 35 ■ Timeseries data or sequence data 35 ■ Image data 36 ■ Video data 37 2.3 The gears of neural networks: tensor operations 38 Element-wise operations 38 ■ Broadcasting 39 ■ Tensor dot 40 ■ Tensor reshaping 42 ■ Geometric interpretation of tensor operations 43 ■ A geometric interpretation of deep learning 44 2.4 The engine of neural networks: gradient-based optimization 46 What’s a derivative? 47 ■ Derivative of a tensor operation: the gradient 48 ■ Stochastic gradient descent 48 Chaining derivatives: the Backpropagation algorithm 51 2.5 Looking back at our first example 53 2.6 Chapter summary 55 3 Getting started with neural networks 56 3.1 Anatomy of a neural network 58 Layers: the building blocks of deep learning 58 ■ Models: networks of layers 59 ■ Loss functions and optimizers: keys to configuring the learning process 60 3.2 Introduction to Keras 61 Keras, TensorFlow, Theano, and CNTK 62 ■ Developing with Keras: a quick overview 62 3.3 Setting up a deep-learning workstation 65 Jupyter notebooks: the preferred way to run deep-learning experiments 65 ■ Getting Keras running: two options 66 Running deep-learning jobs in the cloud: pros and cons 66 What is the best GPUfor deep learning? 66 3.4 Classifying movie reviews: a binary classification example 68 The IMDB dataset 68 ■ Preparing the data 69 Building your network 70 ■ Validating your approach 73 Using a trained network to generate predictions on new data 76 ■ Further experiments 77 ■ Wrapping up 77 3.5 Classifying newswires: a multiclass classification example 78 The Reuters dataset 78 ■ Preparing the data 79 Building your network 79 ■ Validating your approach 80 Generating predictions on new data 83 ■ A different way to handle the labels and the loss 83

FAQs

What fundamental topics are covered before building models?

The book introduces neural network building blocks, including tensor representations and operations, and explains gradient-based optimization with backpropagation. It also covers the machine-learning workflow and core evaluation concepts.

How does the book help readers start training neural networks?

It provides a guided setup for deep-learning workstations using tools such as Jupyter and Keras, then demonstrates training pipelines through classification and regression examples. It emphasizes validating approaches and generating predictions on new data.

What applications are discussed in the practical part?

The practical sections focus on deep learning for computer vision using convolutional networks, and on deep learning for text and sequences. It also covers advanced best practices and generative deep learning approaches.

Jul 6, 2026

Upload by Eliana