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Deep Learning in Practice

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Hardback
1 December 2021
198 Pages
RRP: $219.00
$216.00
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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 and provides examples for the DL algorithms that are presented in this book.
  • Analyzes the DL network’s 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.

You Can Find This Book In

    Book Details
    ISBN-13
    9780367458621
    Author
    Mehdi Ghayoumi
    Publisher
    CRC Press
    Published
    1 December 2021
    Format
    Hardback
    Pages
    198

    This product hasn't received any reviews yet. Be the first to review this product!

    RRP: $219.00
    $216.00
    Hurry up! Current stock:

    Deep Learning in Practice

    RRP: $219.00
    $216.00

    Description

    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 network’s 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.

    You Can Find This Book In

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