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

Object localization

Convolutional neural networks (CNNs) are extremely flexible objects—so far, we have used them to solve classification problems, making them learn to extract features specific to the task. As shown in Chapter 6, Image Classification Using TensorFlow Hub, the standard architecture of CNNs designed to classify images is made of two parts—the feature extractor, which produces a feature vector, and a set of fully connected layers that classifies the feature vector in the (hopefully) correct class:

The classifier placed on top of the feature vector can also be seen as the head of the network

The fact that, so far, CNNs have only been used to solve classification problems should not mislead us. These types of networks are extremely powerful, and, especially in their multilayer setting, they can be used to solve many different kinds of problems, extracting...