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.
Section 1: Neural Network Fundamentals
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