Deep learning in practice

Deep learning in practice

by Ghayoumi, Mehdi

Type Book
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Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains a…

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Title Statements

  • Full title :Deep learning in practice / Mehdi Ghayoumi.

Summary

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network⁰́₉s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.

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Edition Statements

  • Publication Date:2022.
  • Edition:First edition

Publication Statements

  • Publisher(s):CRC Press
  • Place of Publication:Boca Raton, FL
  • Language:eng
  • Publication Date:2022.

The Physical Object

  • Description:xix, 198 pages : : illustrations ; : 25 cm

Edition Identifiers

  • ISBN:9780367458621, 0367458624, 9780367456580, 0367456583
  • oclc:on1267401913
  • LC Classification:Q325.73
  • Dewey Decimal:006.3/1

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