Franklin

Artificial neural networks with TensorFlow 2 : ANN architecture machine learning projects / Poornachandra Sarang.

Author/Creator:
Sarang, Poornachandra, author.
Publication:
[Place of publication not identified] : Apress, [2021]
Format/Description:
Book
1 online resource (XXIX, 726 p. 237 illus.)
Edition:
1st ed. 2021.
Status/Location:
Loading...

Options
Location Notes Your Loan Policy

Details

Subjects:
Neural networks (Computer science).
Machine learning.
TensorFlow.
Form/Genre:
Electronic books.
Summary:
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. You will: Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer.
Contents:
Chapter 1: TensorFlow Jump Start
Chapter 2: A Closer Look at TensorFlow
Chapter 3: Deep Dive in tf.keras
Chapter 4: Transfer Learning
Chapter 5: Neutral Networks for Regression
Chapter 6: Estimators
Chapter 7: Text Generation
Chapter 8: Language Translation
Chapter 9: Natural Langauge
Chapter 10: Image Captioning
Chapter 11: Time Series
Chapter 12: Style Transfer
Chapter 13: Image Generation- Chapter 14: Image Translation.
Notes:
Description based on print version record.
ISBN:
1-4842-6150-X
Publisher Number:
10.1007/978-1-4842-6150-7 doi