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

Deep learning from first principles
Image classification and image segmentation
Timeseries forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color 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 quickly 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. This book shows you how to get started.

About the book
Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded 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 color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications.

What's inside

Deep learning from first principles
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout

• 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 timeseries
  11 Deep learning for text
  12 Generative deep learning
  13 Best practices for the real world
  14 Conclusions