Summary 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. Purchase of the print
book includes a free eBook in PDF, Kindle, and ePub formats from Manning
Publications. About the Technology Machine learning has made remarkable
progress in recent years. We went from near-unusable speech and image
recognition, to near-human 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, natural-language processing, and generative models. By the time you
finish, you'll have the knowledge and hands-on skills to apply deep learning
in your own projects. What's Inside Deep learning from first principlesSetting
up your own deep-learning environment Image-classification modelsDeep learning
for text and sequencesNeural style transfer, text generation, and image
generation About the Reader Readers need intermediate Python skills. No
previous experience with Keras, Tensor Flow, 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 deep-learning library, as well as a
contributor to the Tensor Flow machine-learning framework. He also does deep-
learning 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. Table of Contents PART 1 - FUNDAMENTALS OF
DEEP LEARNING What is deep learning?Before we begin: the mathematical building
blocks of neural networks Getting started with neural networksFundamentals of
machine learningPART 2 - DEEP LEARNING IN PRACTICEDeep learning for computer
visionDeep learning for text and sequencesAdvanced deep-learning best
practicesGenerative deep learningConclusionsappendix A - Installing Keras and
its dependencies on Ubuntuappendix B - Running Jupiter notebooks on an EC2 GPU
instance. Read more