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


In this chapter, we analyzed how TensorFlow works under the hood—the separation between the graph definition phase and its execution within a session, how to use the Python API to interact with a graph, and how to define a model and measure the metrics during training.

It's worth noting that this chapter analyzed how TensorFlow works in its static graph version, which is no longer the default in TensorFlow 2.0; however, the graph is still present and even when used in eager mode, every API call produces operations that can be executed inside a graph to speed up execution. As will be shown in the next chapter, TensorFlow 2.0 still allows models to be defined in static graph mode, especially when defining models using the Estimator API.

Having knowledge of graph representation is of fundamental importance, and having at least an intuitive idea about the advantages...