Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Paolo Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: Paolo Galeone

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.
Table of Contents (15 chapters)
Free Chapter
Section 1: Neural Network Fundamentals
Section 2: TensorFlow Fundamentals
Section 3: The Application of Neural Networks

Section 3: The Application of Neural Networks

This section teaches you how to implement various neural network applications in a variety of domains and demonstrates how powerful neural networks are, especially when used with a good framework such as TensorFlow. At the end of this section, you will have theoretical, as well as practical, knowledge of different neural network architectures, and you will know how to implement them and how to put a model into production using the SavedModel format.

This section comprises the following chapters:

  • Chapter 6, Image Classification Using TensorFlow Hub
  • Chapter 7, Introduction to Object Detection
  • Chapter 8, Semantic Segmentation and Custom Dataset Builder
  • Chapter 9, Generative Adversarial Networks
  • Chapter 10, Bringing a Model to Production