Hands-On Neural Networks with TensorFlow 2.0

Understand TensorFlow, from static graph to eager execution, and design neural networks

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. Les mer
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Paperback
Legg i
Vår pris: 466,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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 DescriptionTensorFlow, 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 forIf 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.

Fakta

Innholdsfortegnelse

Table of Contents

What is Machine Learning?
Neural Networks and Deep Learning
TensorFlow Graph Architecture
TensorFlow 2.0 Architecture
Efficient Data Input Pipelines and Estimator API
Image Classification using TensorFlow Hub
Introduction to Object Detection
Semantic Segmentation and Custom Dataset Builder
Generative Adversarial Networks
Bringing a Model to Production

Om forfatteren

Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy. In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.