Artificial Neural Networks with TensorFlow 2

Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects.

After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis.

This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are.

WHAT YOU'LL LEARN

* Develop Machine Learning Applications
* Translate languages using neural networks
* Compose images with style transfer

WHO THIS BOOK IS FOR

Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies.

POORNACHANDRA SARANG has 30+ years of IT experience and is an experienced author. His work has always focused on state-of-the-art and emerging technologies. He has provided consulting services to—Sun Microsystems, Microsoft, Oracle, and Hewlett-Packard. He has been a Ph.D. advisor for Computer Science and is currently on a Thesis Advisory Committee for students pursuing Ph.D. in Computer Engineering—setting the course curriculum for both under-graduate and post-graduate courses in Computer Science/Engineering. He has delivered seminars, written articles, and provided consulting recently on Machine Learning and Deep Learning. He maintains a machine learning blog at education.abcom.com. Chapter 1: TensorFlow

Introduction

What's new in TensorFlow 2

Chapter 2: A Quick Start on TensorFlow

Hello World for TensorFlow using Google Colab

Chapter 3: TensorFlow Keras Integration

tf.keras

Image Classification

Chapter 4: TensorFlow Hub

Transfer Learning

TensorFlow Hub and Keras

Chapter 5: Regression

Predicting Continuous Value Output

Chapter 6: Estimators

Solving Classification Problems Using Estimators

Chapter 7: Distributed Training

Describing tf.distribute.Strategy

Chapter 8: Text

Text Classification

Generation with RNN

Chapter 9: Language Translation

The seq2seq model for language translation

Chapter 10: Language Understanding

Using Transformer Model

Chapter 11: Image Captioning

Attention-based model for captioning images

Chapter 12: Time Series Forecasting

Using RNNs

Forecasting a univariate/multivariate time series

Chapter 13: Style Transfer

Composing an image in the style of another image

Chapter 14: Image Generation using GAN

Introduction to GAN

Generating images using a DCGAN

Chapter 15: Image Translation

Colorizing B&W images