Hands-On Neural Networks with TensorFlow 2.0

• 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

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.

• 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

Table of contents

1 What is Machine Learning?
The importance of the dataset
Supervised learning
Unsupervised learning
Semi-supervised learning
Summary
Exercises

2 Neural Networks and Deep Learning
Neural networks
Optimization
Convolutional neural networks
Regularization
Summary
Exercises

3 TensorFlow Graph Architecture
Environment setup
Dataflow graphs
Model definition and training
Interacting with the graph using Python
Summary
Exercises

4 TensorFlow 2.0 Architecture
Relearning the framework
The Keras framework and its models
Eager execution and new features
Codebase migration
Summary
Exercises

5 Efficient Data Input Pipelines and Estimator API
Efficient data input pipelines
Estimator API
Summary
Exercises

6 Image Classification Using TensorFlow Hub
Getting the data
Transfer learning
Fine-tuning
Summary
Exercises

7 Introduction to Object Detection
Getting the data
Object localization 
Classification and localization
Summary
Exercises

8 Semantic Segmentation and Custom Dataset Builder
Semantic segmentation
Create a TensorFlow DatasetBuilder
Model training and evaluation
Summary
Exercises

9 Generative Adversarial Networks
Understanding GANs and their applications
Unconditional GANs
Conditional GANs
Summary
Exercises

10 Bringing a Model to Production
The SavedModel serialization format
Python deployment
Supported deployment platforms
Summary
Exercises