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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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

Srinivasan Shanmugam Dec 12, 2023

Colour edition would have been much better for the price, as it gets bored to study this black and while, we are dealing with complex subject like deep learning, image classification n all

Amazon Verified review

Pablo Jordán de la Fuente Sep 08, 2023

Muy bueno

Dwayne M Oct 24, 2021

Best book to learn Tensorflow and Keras and to pass the TensorFlow certification exam.

Conwyn Sep 10, 2021

A very good introduction. A few typographic errors but fairly obvious to spot. Most annoying is the use of acronym without definiton such as Self Driving Car (SDC). I had to look up ontology but generally the authors describe the concepts and algoritms extremely well. Chapter 10 unsupervised learning covers PCA,K-NN and RBM and uses mathematical terms which may be unfamilar. I think a section on Bayes and PDF could be added to the variation autoencoder section (or the mathematics chapter). The best thing is lots of coding examples. For an absolute introduction I still recommend Deep Learning for Coder with fastai and Pytorch but if you have a basic knowledge this book is wider in content and great value.

eddie Sep 07, 2021

Excellent book for understanding state of the art deep learning models with great code examples. Definitely worth the time to explore in full

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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What you will learn

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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

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

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About the 3 authors

FAQs