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  • © 2022

Modern Deep Learning Design and Application Development

Versatile Tools to Solve Deep Learning Problems

Apress

Authors:

(view affiliations)
  • Explains how to think about deep learning from an analytical and creative problem-solving perspective

  • Covers multiple case studies of deep learning architectures

  • Prepares the readers to think critically about the continuously evolving nature of deep learning development

Buying options

eBook
USD 39.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-7413-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 49.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (7 chapters)

  1. Front Matter

    Pages i-xix
  2. A Deep Dive into Keras

    • Andre Ye
    Pages 1-48
  3. The Versatility of Autoencoders

    • Andre Ye
    Pages 115-203
  4. Back Matter

    Pages 441-451

About this book

Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking.  

You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn not only to understand and apply methods successfully but to think critically about it. 

Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems. 

You will:

  • Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization.
  • Compress deep learning models while maintaining performance.
  • Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them.
  • Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.

Keywords

  • Deep Learning
  • Python
  • Neural Network
  • Transfer Learning
  • Model Compression
  • Pruning
  • Pre-trained Model
  • Keras

Authors and Affiliations

  • Redmond, USA

    Andre Ye

About the author

Andre Ye is a data science writer and editor; he has written over 300 data science articles for various top data science publications with over ten million views. He is also a cofounder at Critiq, a peer revision platform that uses machine learning to match users’ essays. In his spare time, Andre enjoys keeping up with current deep learning research, playing the piano, and swimming.

Bibliographic Information

Buying options

eBook
USD 39.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-7413-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 49.99
Price excludes VAT (USA)