Artificial Neural Networks with TensorFlow 2: ANN Architecture Machine Learning Projects - Softcover

Sarang, Poornachandra

Softcover

ISBN 10: 1484261496 ISBN 13: 9781484261491

Publisher: Apress, 2020

Synopsis

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.

About this title

About the Author

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.

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. 

You will:

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

Edition details

• Publisher: Apress
• Publication date: 2020
• Language: English
• ISBN 10: 1484261496
• ISBN 13: 9781484261491
• Binding: Paperback
• Edition number: 1
• Number of pages: 755