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, two of the most widely used high-level APIs were presented. tf.estimator and APIs have maintained almost the same structure they had in TensorFlow 1.x since they were designed with simplicity in mind.

The API, through, allows you to define a high-efficiency data input pipeline by chaining transformations in an ETL fashion, using the method chaining paradigm. objects are integrated with every part of TensorFlow, from eager execution to AutoGraph, passing through the training methods of Keras models and the Estimator API. The ETL process is made easy and the complexity is hidden.

TensorFlow Datasets is the preferred way of creating a new object, and is the perfect tool to use when a machine learning model has been developed, and it is time to measure the performance on every publicly available...