Deep Learning with Python

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(as of Oct 04, 2024 06:13:50 UTC – Details)

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.

From the Publisher

Who should read this book If you’re a data scientist familiar with machine learning, this book will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning If you’re a deep-learning expert looking to get started with the Keras framework, you’ll find this book to be the best Keras crash course available If you’re a graduate student studying deep learning in a formal setting, you’ll find this book to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices

About This Book

This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you’re a practicing machine-learning engineer, a software developer, or a college student, you’ll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. You’ll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems. The code examples use the Python deep-learning framework Keras, with Tensor- Flow as a back-end engine. Keras, one of the most popular and fastest-growing deeplearning frameworks, is widely recommended as the best tool to get started with deep learning.

After reading this book, you’ll have a solid understand of what deep learning is, when it’s applicable, and what its limitations are. You’ll be familiar with the standard workflow for approaching and solving machine-learning problems, and you’ll know how to address commonly encountered issues. You’ll be able to use Keras to tackle real-world problems ranging from computer vision to natural-language processing: image classification, timeseries forecasting, sentiment analysis, image and text generation, and more.

This book is written for people with Python programming experience who want to get started with machine learning and deep learning. But this book can also be valuable to many different types of readers. Even technically minded people who don’t code regularly will find this book useful as an introduction to both basic and advanced deep-learning concepts.

In order to use Keras, you’ll need reasonable Python proficiency. Additionally, familiarity with the Numpy library will be helpful, although it isn’t required. You don’t need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don’t need an advanced mathematics background, either—high school–level mathematics should suffice in order to follow along.

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Deep Learning with Francois Chollet

ASIN ‏ : ‎ 1617294438
Publisher ‏ : ‎ Manning; First Edition (December 22, 2017)
Language ‏ : ‎ English
Paperback ‏ : ‎ 384 pages
ISBN-10 ‏ : ‎ 9781617294433
ISBN-13 ‏ : ‎ 978-1617294433
Item Weight ‏ : ‎ 1.42 pounds
Dimensions ‏ : ‎ 7.38 x 0.8 x 9.25 inches