Book description
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
About the Technology
Machine learning has made remarkable progress in recent years. We went from nearunusable speech and image recognition, to nearhuman accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.
About the Book
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, naturallanguage processing, and generative models. By the time you finish, you'll have the knowledge and handson skills to apply deep learning in your own projects.
What's Inside
 Deep learning from first principles
 Setting up your own deeplearning environment
 Imageclassification models
 Deep learning for text and sequences
 Neural style transfer, text generation, and image generation
About the Reader
Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.
About the Author
François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deeplearning library, as well as a contributor to the TensorFlow machinelearning framework. He also does deeplearning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
Quotes
The clearest explanation of deep learning I have come across...it was a joy to read.
 Richard Tobias, Cephasonics
An excellent handson introductory title, with great depth and breadth.
 David BlumenthalBarby, Babbel
Bridges the gap between the hype and a functioning deeplearning system.
 Peter Rabinovitch, Akamai
The best resource for becoming a master of Keras and deep learning.
 Claudio Rodriguez, Cox Media Group
Publisher resources
Table of contents
 Deep Learning with Python
 Copyright
 Brief Table of Contents
 Table of Contents
 Preface
 Acknowledgments
 About this Book
 About the Author
 About the Cover
 Part 1. Fundamentals of deep learning

Chapter 1. What is deep learning?

1.1. Artificial intelligence, machine learning, and deep learning
 1.1.1. Artificial intelligence
 1.1.2. Machine learning
 1.1.3. Learning representations from data
 1.1.4. The “deep” in deep learning
 1.1.5. Understanding how deep learning works, in three figures
 1.1.6. What deep learning has achieved so far
 1.1.7. Don’t believe the shortterm hype
 1.1.8. The promise of AI
 1.2. Before deep learning: a brief history of machine learning
 1.3. Why deep learning? Why now?

1.1. Artificial intelligence, machine learning, and deep learning

Chapter 2. Before we begin: the mathematical building blocks of neural networks
 2.1. A first look at a neural network

2.2. Data representations for neural networks
 2.2.1. Scalars (0D tensors)
 2.2.2. Vectors (1D tensors)
 2.2.3. Matrices (2D tensors)
 2.2.4. 3D tensors and higherdimensional tensors
 2.2.5. Key attributes
 2.2.6. Manipulating tensors in Numpy
 2.2.7. The notion of data batches
 2.2.8. Realworld examples of data tensors
 2.2.9. Vector data
 2.2.10. Timeseries data or sequence data
 2.2.11. Image data
 2.2.12. Video data
 2.3. The gears of neural networks: tensor operations
 2.4. The engine of neural networks: gradientbased optimization
 2.5. Looking back at our first example

Chapter 3. Getting started with neural networks
 3.1. Anatomy of a neural network
 3.2. Introduction to Keras
 3.3. Setting up a deeplearning workstation
 3.4. Classifying movie reviews: a binary classification example

3.5. Classifying newswires: a multiclass classification example
 3.5.1. The Reuters dataset
 3.5.2. Preparing the data
 3.5.3. Building your network
 3.5.4. Validating your approach
 3.5.5. Generating predictions on new data
 3.5.6. A different way to handle the labels and the loss
 3.5.7. The importance of having sufficiently large intermediate layers
 3.5.8. Further experiments
 3.5.9. Wrapping up
 3.6. Predicting house prices: a regression example

Chapter 4. Fundamentals of machine learning
 4.1. Four branches of machine learning
 4.2. Evaluating machinelearning models
 4.3. Data preprocessing, feature engineering, and feature learning
 4.4. Overfitting and underfitting

4.5. The universal workflow of machine learning
 4.5.1. Defining the problem and assembling a dataset
 4.5.2. Choosing a measure of success
 4.5.3. Deciding on an evaluation protocol
 4.5.4. Preparing your data
 4.5.5. Developing a model that does better than a baseline
 4.5.6. Scaling up: developing a model that overfits
 4.5.7. Regularizing your model and tuning your hyperparameters
 Part 2. Deep learning in practice
 Chapter 5. Deep learning for computer vision

Chapter 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
 6.3.1. A temperatureforecasting problem
 6.3.2. Preparing the data
 6.3.3. A commonsense, nonmachinelearning baseline
 6.3.4. A basic machinelearning approach
 6.3.5. A first recurrent baseline
 6.3.6. Using recurrent dropout to fight overfitting
 6.3.7. Stacking recurrent layers
 6.3.8. Using bidirectional RNNs
 6.3.9. Going even further
 6.3.10. Wrapping up
 6.4. Sequence processing with convnets
 Chapter 7. Advanced deeplearning best practices
 Chapter 8. Generative deep learning
 Chapter 9. Conclusions
 Appendix A. Installing Keras and its dependencies on Ubuntu
 Appendix B. Running Jupyter notebooks on an EC2 GPU instance
 Index
 List of Figures
 List of Tables
 List of Listings
Product information
 Title: Deep Learning with Python
 Author(s):
 Release date: December 2017
 Publisher(s): Manning Publications
 ISBN: 9781617294433
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