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
1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Neural networks

The definition of a neural network, as provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielson, in Neural Network Primer—Part I, is as follows:

"A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs."

In practice, we can think of artificial neural networks as a computational model that is based on how the brain is believed to work. Hence, the mathematical model is inspired by biological neurons.

Biological neurons

The main computational units of the brain are known as neurons; in the human nervous system, approximately 86 billion neurons can be found...