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


Operation research gives us efficient algorithms that we can use to solve optimization problems by finding the global optimum (the global minimum point) if the problems are expressed as a function with well-defined characteristics (for instance, convex optimization requires the function to be a convex).

Artificial neural networks are universal function approximators; therefore, it is not possible to make assumptions about the shape of the function the neural network is approximating. Moreover, the most common optimization methods exploit geometric considerations, but we know from Chapter 1, What is Machine Learning?, that geometry works in an unusual way when dimensionality is high due to the curse of dimensionality.

For these reasons, it is not possible to use operation research methods that are capable of finding the global optimum of an optimization (minimization...