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Home> Data> Machine Learning> TensorFlow 2.0 Quick Start Guide
TensorFlow 2.0 Quick Start Guide
TensorFlow 2.0 Quick Start Guide

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

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Profile Icon Holdroyd
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€17.98€19.99
Full star iconFull star iconHalf star iconEmpty star iconEmpty star icon2.3(3 Ratings)
eBookMar 2019196 pages1st Edition
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€17.98 €19.99
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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date :Mar 29, 2019
Length:196 pages
Edition :1st
Language :English
ISBN-13 :9781789536966
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Publication date :Mar 29, 2019
Length:196 pages
Edition :1st
Language :English
ISBN-13 :9781789536966
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TensorFlow 2.0 Quick Start Guide
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Table of Contents

14 Chapters
Section 1: Introduction to TensorFlow 2.00 AlphaChevron down iconChevron up icon
Section 1: Introduction to TensorFlow 2.00 Alpha
Introducing TensorFlow 2Chevron down iconChevron up icon
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 2Chevron down iconChevron up icon
Keras, a High-Level API for TensorFlow 2
The adoption and advantages of Keras
The features of Keras
The default Keras configuration file
The Keras backend
Keras data types
Keras models
Summary
ANN Technologies Using TensorFlow 2Chevron down iconChevron up icon
ANN Technologies Using TensorFlow 2
Presenting data to an ANN
One-hot encoding
Layers
Activation functions
Creating the model
Gradient calculations for gradient descent algorithms
Loss functions
Summary
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 AlphaChevron down iconChevron up icon
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
Supervised Machine Learning Using TensorFlow 2Chevron down iconChevron up icon
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 2Chevron down iconChevron up icon
Unsupervised Learning Using TensorFlow 2
Autoencoders
Summary
Section 3: Neural Network Applications of TensorFlow 2.00 AlphaChevron down iconChevron up icon
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
Recognizing Images with TensorFlow 2Chevron down iconChevron up icon
Recognizing Images with TensorFlow 2
Quick Draw – image classification using TensorFlow
CIFAR 10 image classification using TensorFlow
Summary
Neural Style Transfer Using TensorFlow 2Chevron down iconChevron up icon
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 2Chevron down iconChevron up icon
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 HubChevron down iconChevron up icon
TensorFlow Estimators and TensorFlow Hub
TensorFlow Estimators
TensorFlow Hub
Summary
Converting from tf1.12 to tf2Chevron down iconChevron up icon
Converting from tf1.12 to tf2
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Customer reviews

Rating distribution
Full star iconFull star iconHalf star iconEmpty star iconEmpty star icon2.3
(3 Ratings)
5 star33.3%
4 star0%
3 star0%
2 star0%
1 star66.7%
Amazon KundeAug 18, 2019
Full star iconFull star iconFull star iconFull star iconFull star icon5
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 reviewAmazon
Yuhong WangJun 06, 2019
Full star iconEmpty star iconEmpty star iconEmpty star iconEmpty star icon1
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 reviewAmazon
Amazon CustomerApr 26, 2020
Full star iconEmpty star iconEmpty star iconEmpty star iconEmpty star icon1
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 reviewAmazon

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About the author

Profile icon Holdroyd
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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