Angular and Deep Learning Pocket Primer

By: Oswald Campesato

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Paperback | 16 November 2020

At a Glance

Paperback
360 Pages

Dimensions(cm)
22.23 x 14.61 x 1.91

Paperback

RRP $65.92

$59.75

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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.

The companion files are also available online by emailing the publisher with proof of purchase at info@merclearning.com.

Industry Reviews

Bookwatch:

"Adding to the Pocket Primer series is a fine introduction to basic deep learning approaches to Angular 10 applications, offering computer users a fast way to applying knowledge to real-world activities. Computer users should expect discussions of basic deep learning concepts, accompanied by algorithms and code files that demonstrate how these concepts work in the Angular 10 environment. Chapters cover TensorFlow 2 and Keras as they examine subjects such as pipes and UI controls, data binding models, architectures for deep learning, and creating histograms, heat maps, and more. Those seeking a quick learning approach to Angular and the deep learning environment will find this pocket primer''s examples and references lend nicely to refresher courses and new introductions alike."

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