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TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0

Holdroyd

€24.99

2.3 (3 Ratings)

Paperback Mar 2019 196 pages 1st Edition

Holdroyd

€24.99

2.3 (3 Ratings)

Paperback Mar 2019 196 pages 1st Edition

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Key benefits

Train your own models for effective prediction, using high-level Keras API
Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks
Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha

Description

TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.

Who is this book for?

Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.

What you will learn

Use tf.Keras for fast prototyping, building, and training deep learning neural network models
Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files
Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications
Understand image recognition techniques using TensorFlow
Perform neural style transfer for image hybridization using a neural network
Code a recurrent neural network in TensorFlow to perform text-style generation

Estimated delivery fee Deliver to Malta

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What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Estimated delivery fee Deliver to Malta

Premium delivery 7 - 10 business days

€32.95

(Includes tracking information)

Product Details

Publication date : Mar 29, 2019

Length: 196 pages

Edition : 1st

Language : English

ISBN-13 : 9781789530759

Category :

Data

Languages :

Python

Concepts :

Machine Learning

Tools :

TensorFlow

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14 Chapters

Section 1: Introduction to TensorFlow 2.00 Alpha

Introducing TensorFlow 2

Looking at the modern TensorFlow ecosystem

Installing TensorFlow

Housekeeping and eager operations

Providing useful TensorFlow operations

Summary

Keras, a High-Level API for TensorFlow 2

The adoption and advantages of Keras

The features of Keras

The default Keras configuration file

ANN Technologies Using TensorFlow 2

Presenting data to an ANN

Gradient calculations for gradient descent algorithms

Loss functions

Summary

Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha

Supervised Machine Learning Using TensorFlow 2

Supervised learning

Linear regression

Our first linear regression example

The Boston housing dataset

Logistic regression (classification)

k-Nearest Neighbors (KNN)

Summary

Unsupervised Learning Using TensorFlow 2

Autoencoders

Summary

Section 3: Neural Network Applications of TensorFlow 2.00 Alpha

Recognizing Images with TensorFlow 2

Quick Draw – image classification using TensorFlow

CIFAR 10 image classification using TensorFlow

Summary

Neural Style Transfer Using TensorFlow 2

Setting up the imports

Preprocessing the images

Viewing the original images

Using the VGG19 architecture

Creating the model

Calculating the losses

Performing the style transfer

Final displays

Summary

Recurrent Neural Networks Using TensorFlow 2

Neural network processing modes

Recurrent architectures

An application of RNNs

The code for our RNN example

Building and instantiating our model

Using our model to get predictions

Summary

TensorFlow Estimators and TensorFlow Hub

TensorFlow Estimators

TensorFlow Hub

Summary

Converting from tf1.12 to tf2

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Customer reviews

Rating distribution

2.3

(3 Ratings)

5 star 33.3%

4 star 0%

3 star 0%

2 star 0%

1 star 66.7%

Amazon Kunde Aug 18, 2019

It's a great book with a lot of well explained examples. It starts with small simple examples and got more complex in the end. So you will get a good feeling how things work out in TensorFlow 2.0 .

Amazon Verified review

Yuhong Wang Jun 06, 2019

I got the kindle version, read the first a few chapters, and returned. As my title said, the tutorials from tensorflow.org and other web sites are more informational.

Amazon Verified review

Amazon Customer Apr 26, 2020

If you're familiar with TensorFlow already,and this book might be a guide to do a quick review on TF 2.0. Strongly not recommend to the user who is also new to TensorFlow... too many errors and also some code cannot run at all... I just follow the content of the book and still rely on TensorFlow official documents for coding part.... so not recommend it at all....

Amazon Verified review

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About the author

Holdroyd

Tony Holdroyd's first degree, from Durham University, was in maths and physics. He also has technical qualifications, including MCSD, MCSD.net, and SCJP. He holds an MSc in computer science from London University. He was a senior lecturer in computer science and maths in further education, designing and delivering programming courses in many languages, including C, C+, Java, C#, and SQL. His passion for neural networks stems from research he did for his MSc thesis. He has developed numerous machine learning, neural network, and deep learning applications, and has advised in the media industry on deep learning as applied to image and music processing. Tony lives in Gravesend, Kent, UK, with his wife, Sue McCreeth, who is a renowned musician.

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TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0

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