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

Galeone

NZ$35.99 ~~NZ$51.99~~

3.7 (7 Ratings)

eBook Sep 2019 358 pages 1st Edition

eBook

NZ$35.99 ~~NZ$51.99~~

Galeone

NZ$35.99 ~~NZ$51.99~~

3.7 (7 Ratings)

eBook Sep 2019 358 pages 1st Edition

eBook

NZ$35.99 ~~NZ$51.99~~

NZ$35.99 ~~NZ$51.99~~

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Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : Sep 18, 2019

Length: 358 pages

Edition : 1st

Language : English

ISBN-13 : 9781789613797

Vendor :

Google

Category :

Data

Languages :

Python

Concepts :

Neural Networks

Tools :

TensorFlow

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14 Chapters

Section 1: Neural Network Fundamentals

What is Machine Learning?

The importance of the dataset

Supervised learning

Unsupervised learning

Semi-supervised learning

Summary

Exercises

Neural Networks and Deep Learning

Neural networks

Optimization

Convolutional neural networks

Regularization

Summary

Exercises

Section 2: TensorFlow Fundamentals

TensorFlow Graph Architecture

Environment setup

Dataflow graphs

Model definition and training

Interacting with the graph using Python

Summary

Exercises

TensorFlow 2.0 Architecture

Relearning the framework

The Keras framework and its models

Eager execution and new features

Codebase migration

Summary

Exercises

Efficient Data Input Pipelines and Estimator API

Efficient data input pipelines

Estimator API

Summary

Exercises

Section 3: The Application of Neural Networks

Image Classification Using TensorFlow Hub

Introduction to Object Detection

Getting the data

Object localization

Classification and localization

Summary

Exercises

Semantic Segmentation and Custom Dataset Builder

Semantic segmentation

Create a TensorFlow DatasetBuilder

Model training and evaluation

Summary

Exercises

Generative Adversarial Networks

Understanding GANs and their applications

Bringing a Model to Production

The SavedModel serialization format

Python deployment

Supported deployment platforms

Summary

Exercises

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About the author

Galeone

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.

See other products by Galeone

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

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