Deep Learning with Python (2 ed)

Deep Learning with Python, Second Edition

Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn:

• Deep learning from first principles
• Image classification & image segmentation
• Time series forecasting
• Text classification and machine translation
• Text generation, neural style transfer, and image generation

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

About the technology

Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science.

About the book

Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You’ll pick up the skills to start developing deep-learning applications.

About the reader

For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

Table of Contents

1  What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to Keras and TensorFlow
4 Getting started with neural networks: Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 Working with Keras: A deep dive
8 Introduction to deep learning for computer vision
9 Advanced deep learning for computer vision
10 Deep learning for time series
11 Deep learning for text
12 Generative deep learning
13 Best practices for the real world
14 Conclusions