- Paperback: 384 pages
- Publisher: Manning Publications; 1 edition (December 22, 2017)
- Language: English
- ISBN-10: 1617294438
- ISBN-13: 978-1617294433
- Product Dimensions: 7.2 x 0.8 x 9.2 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 14 customer reviews
- Amazon Best Sellers Rank: #3,602 in Books (See Top 100 in Books)
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Deep Learning with Python 1st Edition
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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.
About the Author
Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.
Top customer reviews
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I cannot say enough about how good this book really is. If you can only buy one book on deep learning, this should be the one you buy.
Deep Learning with Python will walk you through it all. I was able to learn way of implementing DL to areas I was trying to break through before with a lot of ease. It also walks you through how to use DL on a Cloud Environment, which is going to be an invaluable tool for your career if it's anywhere related to software.
Keras (the framework you'll use in this book) is a great tool for you to start experimenting and understanding the concepts of Neural Networks. And you will be able to build your own networks after a few code examples from the book.
Things this book will give you:
- A great framework for you to experiment, craft, learn, and understand more about Neural Networks.
- A great set of examples so that you can see how you can adapt DL to your specific domain problem.
- Cloud knowledge on how to leverage it to use Deep Learning
I'm going through my second reading of the book, taking advantage of Colaboratory, a Google research project created to help disseminate machine learning education and research.
The book is the ideal for starters who have no background in Deep Learning but have worked with ML algorithms. It explains all the basic fundamental building blocks necessary to understand, develop and run in production a NN based approach to solving problems.
The book clearly explains how these systems work and what makes them tick.
The book is filled with working code and extremely good explanation of the code and to what is being done and why and in most places why certain parameters are chosen.
I highly recommend this book for starters and intermediate level Deep Learning professionals