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MERCURY LEARNING and INFORMATION provides print and digital content in the areas of science and medicine, technology and computing, engineering, and mathematics (STEM disciplines) designed for the professional/reference, trade, library, higher education, career school, and online training markets. We embrace both traditional and innovative publishing models designed to accommodate the needs of the intended audience by using the appropriate delivery methods.
Angular And Deep Learning Pocket Primer
ISBN: 9781683924739
Pub Date: October 2020
Specs: 7 x 9 Paperback
Pages: 250
Price: $39.95
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic deep learning concepts and incorporate that knowledge into Angular 10 applications. It is intended to be a fast-paced introduction to some basic features of deep learning and an overview of several popular deep learning classifiers. The book includes code samples and numerous figures and covers topics such as Angular 10 functionality, basic deep learning concepts, classification algorithms, TensorFlow, and Keras. Companion files with code and color figures are included.
FEATURES
- Introduces basic deep learning concepts and Angular 10 applications
- Covers MLPs (MultiLayer Perceptrons) and CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), GRUs (Gated Recurrent Units), autoencoders, and GANs (Generative Adversarial Networks)
- Introduces TensorFlow 2 and Keras
- Includes companion files with source code and 4-color figures
BRIEF TOC
1: Quick Introduction to Angular. 2: UI Controls, User Input, and Pipes. 3: Forms and Services. 4: Deep Learning Introduction. 5: Deep Learning: RNNs and LSTMs. 6: Angular and TensorFlow.js. Appendices: A. Introduction to Keras. B. Introduction to TensorFlow 2. C. TensorFlow 2 Datasets. Index.
ABOUT THE AUTHOR
Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Java, Android, and TensorFlow. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the Python Pocket Primer (Mercury Learning).