This book is meant for data scientists, machine learning developers, deep learning researchers, and developers with a basic statistical background who want to work with neural networks and discover the TensorFlow structure and its new features. A working knowledge of the Python programming language is required to get the most out of the book.
Hands-On Neural Networks with TensorFlow 2.0
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Hands-On Neural Networks with TensorFlow 2.0
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Overview of this book
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.
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Current Title:
Hands-On Neural Networks with TensorFlow 2.0
Table of Contents (15 chapters)
Preface
What is Machine Learning?
Neural Networks and Deep Learning
Section 2: TensorFlow Fundamentals
TensorFlow Graph Architecture
TensorFlow 2.0 Architecture
Efficient Data Input Pipelines and Estimator API
Section 3: The Application of Neural Networks
Image Classification Using TensorFlow Hub
Introduction to Object Detection
Semantic Segmentation and Custom Dataset Builder
Generative Adversarial Networks
Bringing a Model to Production
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