Introduction to Deep Learning Business Applications for Developers

Introduction to Deep Learning Business Applications for Developers: From Conversational Bots in Customer Service to Medical Image Processing

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.

What You Will Learn

• 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

Who This Book Is For

Data scientists, entrepreneurs, and business developers.

Table of Contents

Part I: Background and Fundamentals
Chapter 1: Introduction
Chapter 2: Deep Learning: An Overview
Chapter 3: Deep Neural Network Models

Part II: Deep Learning: Core Applications
Chapter 4: Image Processing
Chapter 5: Natural Language Processing and Speech
Chapter 6: Reinforcement Learning and Robotics

Part III: Deep Learning: Business Applications
Chapter 7: Recommendation Algorithms and E-commerce
Chapter 8: Games and Art
Chapter 9: Other Applications

Part IV: Opportunities and Perspectives
Chapter 10: Business Impact of DL Technology
Chapter 11: New Research and Future Directions

Appendix A: Training DNN with Keras