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Hands-On Neural Networks with TensorFlow 2.0

By : Galeone

3.7 (7)

Buy this Book

Hands-On Neural Networks with TensorFlow 2.0

3.7 (7)

By: Galeone

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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Section 1: Neural Network Fundamentals

What is Machine Learning?

The importance of the dataset

Supervised learning

Unsupervised learning

Semi-supervised learning

Summary

Exercises

Neural Networks and Deep Learning

Neural networks

Optimization

Convolutional neural networks

Regularization

Summary

Exercises

Section 2: TensorFlow Fundamentals

TensorFlow Graph Architecture

Environment setup

Dataflow graphs

Model definition and training

Interacting with the graph using Python

Summary

Exercises

TensorFlow 2.0 Architecture

Relearning the framework

The Keras framework and its models

Eager execution and new features

Codebase migration

Summary

Exercises

Efficient Data Input Pipelines and Estimator API

Efficient data input pipelines

Estimator API

Summary

Exercises

Section 3: The Application of Neural Networks

Image Classification Using TensorFlow Hub

Getting the data

Transfer learning

Fine-tuning

Summary

Exercises

Introduction to Object Detection

Getting the data

Object localization

Classification and localization

Summary

Exercises

Semantic Segmentation and Custom Dataset Builder

Semantic segmentation

Create a TensorFlow DatasetBuilder

Model training and evaluation

Summary

Exercises

Generative Adversarial Networks

Understanding GANs and their applications

Unconditional GANs

Conditional GANs

Summary

Exercises

Bringing a Model to Production

The SavedModel serialization format

Python deployment

Supported deployment platforms

Summary

Exercises

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Interacting with the graph using Python

Python is the language of choice to train a TensorFlow model; however, after defining a computational graph in Python, there are no constraints regarding using it with another language to execute the learning operations defined.

Always keep in mind that we use Python to define a graph and this definition can be exported in a portable and language-agnostic representation (Protobuf)—this representation can then be used in any other language to create a concrete graph and using it within a session.

The TensorFlow Python API is complete and easy to use. Therefore, we can extend the previous example to measure the accuracy (defining the accuracy measurement operation in the graph) and use this metric to perform model selection.

Selecting the best model means storing the model parameters at the end of each epoch and moving the parameters...