Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API , Second Edition

Antonio Gulli

Amita Kapoor

Sujit Pal

$43.99

4.3 (26 Ratings)

Paperback Dec 2019 646 pages 2nd Edition

Antonio Gulli

Amita Kapoor

Sujit Pal

$43.99

4.3 (26 Ratings)

Paperback Dec 2019 646 pages 2nd Edition

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Deep Learning with TensorFlow 2 and Keras

TensorFlow 1.x and 2.x

The intent of this chapter is to explain the differences between TensorFlow 1.x and TensorFlow 2.0. We'll start by reviewing the traditional programming paradigm for 1.x and then we'll move on to all the new features and paradigms available in 2.x.

Understanding TensorFlow 1.x

It is generally the tradition that the first program one learns to write in any computer language is "hello world." We maintain the convention in this book! Let's begin with a Hello World program:

import tensorflow as tf
message = tf.constant('Welcome to the exciting world of Deep Neural Networks!')
with tf.Session() as sess:
    print(sess.run(message).decode())

Let us go in depth into this simple code. The first line imports tensorflow. The second line defines the message using tf.constant. The third line defines the Session() using with, and the fourth runs the session using run(). Note that this tells us that the result is a "byte string." In order to remove string quotes and b (for byte) we use the method decode().

TensorFlow 1.x computational graph program structure

TensorFlow 1.x is unlike other programming languages. We first need to build a blueprint of whatever neural network we want...

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Key benefits

Introduces and then uses TensorFlow 2 and Keras right from the start
Teaches key machine and deep learning techniques
Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples

Description

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

Who is this book for?

This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

What you will learn

Build machine learning and deep learning systems with TensorFlow 2 and the Keras API
Use Regression analysis, the most popular approach to machine learning
Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers
Use GANs (generative adversarial networks) to create new data that fits with existing patterns
Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another
Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response
Train your models on the cloud and put TF to work in real environments
Explore how Google tools can automate simple ML workflows without the need for complex modeling

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Amazon verified reviews

Ana Maria Simionovici Apr 06, 2020

I started reading the book few weeks ago. I must say it is lovely and nicely written. It is easier for to read it after being in touch with Keras, fastai(build on top pytorch). Of course, with some machine learning background things can go smoothly. My recommandation would be to dig in well the first chapter as it has the base concepts of machine learning. I do recommend it! And I love it!

Amazon Verified review

seda cavdaroglu Feb 19, 2020

I really liked this book which covers all modern Deep Learning concepts with practical applications based on Tensorflow 2. The chapters are clear and easy to follow, but their content is always valuable. I suggest this book to everyone who wants to start her journey in Deep Learning. It's worth all pennies and brings an excellent reference to young and seasoned practicioners.

Kay T Dec 18, 2020

This is a very well written, comprehensive book on deep learning as a technique to solve various machine learning problems. Its outline is quite thorough. The content will definitely remain relevant for a long time. The three authors are recognized as leading authorities in TensorFlow. The content and coverage are definitely timely and well-conceived. What I like about this book is the coverage for the basics. If you have limited understanding or just start with deep learning, the first two chapters teach you enough of background for you to move into the core of deep learning techniques, starting with regression and classification, and then the more complicated model architectures such as CNN, RNN and GAN.This book is very well balanced in terms of topic coverage. The first two chapters enable you to grasp the fundamentals of deep learning and TensorFlow 2.X semantics using the tf.keras API. You will find all the code and examples to be very practical and with well articulated explanations. If you are looking for a comprehensive guide on deep learning with practical examples, then this book is the right choice.

Maruko Feb 26, 2020

Probabilmente il miglior libro in circolazione sull'argomento.Vale ogni centesimo pagato.

@drakpz Feb 15, 2020

The key is in the book’s title: flow. Yes, that’s my very own (100% bio/natural ;-) ) neural network eventually got to when trying to concisely describe this book. Given the non-triviality of the topics that the authors wrote about, that alone is a remarkable outcome IMHO. There’s a subtle though absolutely pragmatic approach in every chapter that guides the reader’s reasoning to a double win: grasping the inner value of the core concepts and quickly gaining real world examples (through code). I also found the vast majority of chapters to be almost ‘self consistent’: although some cornerstones are required (and thoroughly dealt with in the first few chapters) you’ll find yourself jumping back straight to, say, GANs or AutoML focused chapters for future reference or deeper dives. The ‘math focused’ chapter is an added bonus which, although not stricty necessary for the book’s mission, deserves its own credit and will give you some extra ‘Ah!’ moments.

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API , Second Edition

What do you get with Print?

Deep Learning with TensorFlow 2 and Keras

TensorFlow 1.x and 2.x

Understanding TensorFlow 1.x

TensorFlow 1.x computational graph program structure

Understanding TensorFlow 2.x

Eager execution

The TensorFlow 2.x ecosystem

Keras or tf.keras?

Summary

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Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API , Second Edition

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