This book is a guide to the TensorFlow (TF) framework, from the static graph architecture of TF 1.x to the eager execution and all the new features introduced in TF 2.0. Neural Networks applications are developed throughout the book with the aim of making the reader capable of developing neural networks-based solutions to real problems using TF 2.0

A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0

Key Features

• Understand the basics of machine learning and discover the power of neural networks and deep learning
• Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0
• Solve any deep learning problem by developing neural network-based solutions using TF 2.0

Book Description

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.

What you will learn

• Grasp machine learning and neural network techniques to solve challenging tasks
• Apply the new features of TF 2.0 to speed up development
• Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines
• Perform transfer learning and fine-tuning with TensorFlow Hub
• Define and train networks to solve object detection and semantic segmentation problems
• Train Generative Adversarial Networks (GANs) to generate images and data distributions
• Use the SavedModel file format to put a model, or a generic computational graph, into production

Who this book is for

If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful.

Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.