Book Image

TensorFlow 2.0 Quick Start Guide

By : Tony Holdroyd
Book Image

TensorFlow 2.0 Quick Start Guide

By: Tony Holdroyd

Overview of this book

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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Introduction to TensorFlow 2.00 Alpha
Section 1: Introduction to TensorFlow 2.00 Alpha
2
Introducing TensorFlow 2
Introducing TensorFlow 2
Looking at the modern TensorFlow ecosystem
Installing TensorFlow
Housekeeping and eager operations
Providing useful TensorFlow operations
Summary
3
Keras, a High-Level API for TensorFlow 2
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
4
ANN Technologies Using TensorFlow 2
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
5
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
6
Supervised Machine Learning Using TensorFlow 2
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
7
Unsupervised Learning Using TensorFlow 2
Unsupervised Learning Using TensorFlow 2
Autoencoders
Summary
8
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
9
Recognizing Images with TensorFlow 2
Recognizing Images with TensorFlow 2
Quick Draw – image classification using TensorFlow
CIFAR 10 image classification using TensorFlow
Summary
10
Neural Style Transfer Using TensorFlow 2
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
11
Recurrent Neural Networks Using TensorFlow 2
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
12
TensorFlow Estimators and TensorFlow Hub
TensorFlow Estimators and TensorFlow Hub
TensorFlow Estimators
TensorFlow Hub
Summary
13
Converting from tf1.12 to tf2
Converting from tf1.12 to tf2
14
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Who this book is for

As its title suggests, this book has been written to introduce readers to TensorFlow and many of its latest features, up to and including version 2.0.0 alpha, including eager execution, tf.data, tf.keras, TensorFlow Hub, machine learning, and neural network applications.

This book is intended to be useful for anyone with some exposure to machine learning and its applications: data scientists, machine learning engineers, computer scientists, computer science students, and hobbyists.

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