Hands-On Neural Networks with TensorFlow 2.0
Understand TensorFlow, from static graph to eager execution, and design neural networks
Language - English
Updated on Oct, 2020
About the Book
Book description
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.

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Author Details

Packt Publishing
Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.
Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.
As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.
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