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Introduction to Deep Learning Business Applications for Developers

From Conversational Bots in Customer Service to Medical Image Processing

  • Armando Vieira
  • Bernardete Ribeiro

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Background and Fundamentals

    1. Front Matter
      Pages 1-1
    2. Armando Vieira, Bernardete Ribeiro
      Pages 3-7
    3. Armando Vieira, Bernardete Ribeiro
      Pages 9-35
    4. Armando Vieira, Bernardete Ribeiro
      Pages 37-73
  3. Deep Learning: Core Applications

    1. Front Matter
      Pages 75-75
    2. Armando Vieira, Bernardete Ribeiro
      Pages 77-109
    3. Armando Vieira, Bernardete Ribeiro
      Pages 111-136
    4. Armando Vieira, Bernardete Ribeiro
      Pages 137-168
  4. Deep Learning: Business Applications

    1. Front Matter
      Pages 169-169
    2. Armando Vieira, Bernardete Ribeiro
      Pages 171-184
    3. Armando Vieira, Bernardete Ribeiro
      Pages 185-205
    4. Armando Vieira, Bernardete Ribeiro
      Pages 207-233
  5. Opportunities and Perspectives

    1. Front Matter
      Pages 235-235
    2. Armando Vieira, Bernardete Ribeiro
      Pages 237-254
    3. Armando Vieira, Bernardete Ribeiro
      Pages 255-285
  6. Back Matter
    Pages 287-343

About this book

Introduction

Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. 

An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You’ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer.

After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework.

You will:

  • Find out about deep learning and why it is so powerful
  • Work with the major algorithms available to train deep learning models
  • See the major breakthroughs in terms of applications of deep learning  
  • Run simple examples with a selection of deep learning libraries 
  • Discover the areas of impact of deep learning in business

Keywords

Deep Learning Deep Neural Networks Natural Language Processing Convolutional Neural Networks Robotics Recommendation Alorithms Reinforcement Learning Python

Authors and affiliations

  • Armando Vieira
    • 1
  • Bernardete Ribeiro
    • 2
  1. 1.LinköpingSweden
  2. 2.CoimbraPortugal

Bibliographic information

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