Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks
Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks by Paolo Galeone
English | 2019 | ISBN: 1789615555 | 358 Pages | EPUB | 23 MB

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