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Hands-On Neural Networks with TensorFlow 2.0
Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

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Hands-On Neural Networks with TensorFlow 2.0

Section 1: Neural Network Fundamentals

This section provides a basic introduction to machine learning and the important concepts of neural networks and deep learning.

This section comprises the following chapters:

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Key benefits

  • Understand the basics of machine learning and discover the power of neural networks and deep learning
  • Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0
  • Solve any deep learning problem by developing neural network-based solutions using TF 2.0

Description

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.

Who is this book for?

If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful. Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.

What you will learn

  • Grasp machine learning and neural network techniques to solve challenging tasks
  • Apply the new features of TF 2.0 to speed up development
  • Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines
  • Perform transfer learning and fine-tuning with TensorFlow Hub
  • Define and train networks to solve object detection and semantic segmentation problems
  • Train Generative Adversarial Networks (GANs) to generate images and data distributions
  • Use the SavedModel file format to put a model, or a generic computational graph, into production
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 18, 2019
Length: 358 pages
Edition : 1st
Language : English
ISBN-13 : 9781789615555
Vendor :
Google
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Estimated delivery fee Deliver to Brazil

Standard delivery 10 - 13 business days

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R$203.95
(Includes tracking information)

Product Details

Publication date : Sep 18, 2019
Length: 358 pages
Edition : 1st
Language : English
ISBN-13 : 9781789615555
Vendor :
Google
Category :
Languages :
Tools :

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Table of Contents

14 Chapters
Section 1: Neural Network Fundamentals Chevron down icon Chevron up icon
What is Machine Learning? Chevron down icon Chevron up icon
Neural Networks and Deep Learning Chevron down icon Chevron up icon
Section 2: TensorFlow Fundamentals Chevron down icon Chevron up icon
TensorFlow Graph Architecture Chevron down icon Chevron up icon
TensorFlow 2.0 Architecture Chevron down icon Chevron up icon
Efficient Data Input Pipelines and Estimator API Chevron down icon Chevron up icon
Section 3: The Application of Neural Networks Chevron down icon Chevron up icon
Image Classification Using TensorFlow Hub Chevron down icon Chevron up icon
Introduction to Object Detection Chevron down icon Chevron up icon
Semantic Segmentation and Custom Dataset Builder Chevron down icon Chevron up icon
Generative Adversarial Networks Chevron down icon Chevron up icon
Bringing a Model to Production Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(7 Ratings)
5 star 57.1%
4 star 14.3%
3 star 0%
2 star 0%
1 star 28.6%
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Aundrea Mar 12, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is very pragmatic and that is why I recommend it. It has many eye opening examples on how to use TensorFlow 2.0. It is a great resource if you really want to understand neural networks and deep learning. I have other books on machine learning and TensorFlow but none of them explain things as clearly as this book.
Amazon Verified review Amazon
fasttouch Nov 17, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great book on TensorFlow 2.0. All the book is well written and explain the concepts really well. It is extremely precise and goes really deep in technical details. Especially Chapter 3 and 4 are fantastic. If you want to know how TensorFlow works this book is for you.
Amazon Verified review Amazon
joe hoeller Jan 19, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent in-depth resource for beginners that have never been exposed to Deep Learning in TensorFlow 2.0, with some of the material extending upon that and taking the beginner to an intermediate level.I recommend coding examples from hand while creating notes above the code line for line that summarize in 1-2 sentences what each line of code does.This will reinforce what you learn while developing familiarity and experience with the Tensorflow version 2.0 API.Well written and executed for this level and within the context of deep learning and neural networks.
Amazon Verified review Amazon
Grigory Sapunov Jan 08, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a good introductory book to start with TF 1.0 and 2.0. The book is not too general as I thought from some reviews, it contains a lot of relevant topics required to meaningfully start doing ML/DL projects. Just look at the contents. For example, it has an intro into ML itself, enough deep learning basics, all the relevant TensorFlow stuff, highlighting the differences between 1.0 and 2.0, explains new stuff like TF Hub, describes all relevant metrics for different tasks and so on. Examples range from basic image classification to more advanced object detection and even semantic segmentation, not forgetting a basic intro into GANs. So, I like it. It's definitely not a typical book about nothing, it's a good introductory book to quickly start learning this field. It will save you a lot of time comparing to reading different pages and posts in the internet.
Amazon Verified review Amazon
Placeholder Jan 02, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This books explains end to end new features added in TF2. Using Gradient Tape, writing custom training loop. I
Amazon Verified review Amazon
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