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

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Book:
Introduction to Deep Learning Business Applications for Developers: From Conversational Bots in Customer Service to Medical Image Processing
Author:
Armando Vieira / Bernardete Ribeiro
Publisher:
Apress
Genre:
Books / Home and family
Year:
2018
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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. Youll 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.

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Part I
Background and Fundamentals

Armando Vieira, Bernardete Ribeiro 2018

Armando Vieira and Bernardete Ribeiro Introduction to Deep Learning Business Applications for Developers

1. Introduction

Armando Vieira 1 and Bernardete Ribeiro 2

(1)

Linkping, Sweden

(2)

Coimbra, Portugal

This chapter will describe what the book is about, the books goals and audience, why artificial intelligence (AI) is important, and how the topic will be tackled.

Teaching computers to learn from experience and make sense of the world is the goal of artificial intelligence. Although people do not understand fully how the brain is capable of this remarkable feat, it is generally accepted that AI should rely on weakly supervised generation of hierarchical abstract concepts of the world. The development of algorithms capable of learning with minimal supervisionlike babies learn to make sense of the world by themselvesseems to be the key to creating truly general artificial intelligence (GAI) [GBC16].

Artificial intelligence is a relatively new area of research (it started in the 1950s) that has had some successes and many failures. The initial enthusiasm, which originated at the time of the first electronic computer, soon faded away with the realization that most problems that the brain solves in a blink of an eye are in fact very hard to solve by machines. These problems include locomotion in uncontrolled environments, language translation, and voice and image recognition. Despite many attempts, it also became clear that the traditional (rule-based and descriptive) approach to solving complex mathematical equations or even proving theorems was insufficient to solve the most basic situations that a 2-year-old toddler had no difficulty with, such as understanding basic language concepts. This fact led to the so-called long AI winter, where many researchers simply gave up creating machines with human-level cognitive capabilities, despite some successes in between, such as the IBM machine Deep Blue that become the best chess player in the world or such as the application of neural networks for handwritten digit recognition in late 1980s.

AI is today one of the most exciting research fields with plenty of practical applications, including autonomous vehicles, drug discovery, robotics, language translation, and games. Challenges that seemed insurmountable just a decade ago have been solvedsometimes with superhuman accuracyand are now present in products and ubiquitous applications. Examples include voice recognition, navigation systems, facial emotion detection, and even art creation, such as music and painting. For the first time, AI is leaving the research labs and materializing in products that could have emerged from science-fiction movies.

How did this revolution become possible in such a short period of time? What changed in recent years that puts us closer to the GAI dream? The answer is more a gradual improvement of algorithms and hardware than a single breakthrough. But certainly deep neural networks , commonly referred to as deep learning (DL), appears at the top of the list [J15].

1.1 Scope and Motivation

Advances in computational power, big data, and the Internet of Things are powering the major transformation in technology and are powering productivity across all industries.

Through examples in this book, you will explore concrete situations where DL is advantageous with respect to other traditional (shallow) machine learning algorithms, such as content-based recommendation algorithms and natural language processing. Youll learn about techniques such as Word2vec, skip-thought vectors, and Item2Vec. You will also consider recurrent neural networks trained with stacked long short-term memory (LSTM) units and sequence2sequence models for language translation with embeddings.

A key feature of DL algorithms is their capability to learn from large amounts of data with minimal supervision, contrary to shallow models that normally require less (labeled) data. In this book, you will explore some examples, such as video prediction and image segmentation, with fully convolutional neural networks (FCNNs) and residual neural networks (ResNets) that have achieved top performance in the ImageNet image recognition competition. You will explore the business implications of these image recognition techniques and some active startups in this very active field.

The implications of DL-supported AI in business is tremendous, shaking to the foundations many industries. It is perhaps the biggest transformative force since the Internet.

This book will present some applications of DL models for financial risk assessment (credit risk with deep belief networks and options optimizations with variational auto-encoder). You will briefly explore applications of DL to control and robotics and learn about the DeepQ learning algorithm (which was used to beat humans in the game Go) and actor-critic methods for reinforcement learning.

You will also explore a recent and powerful set of algorithms, named generative adversarial neural networks (GANs) , including the dcGAN, the conditional GAN, and the pixel2pixel GAN. These are very efficient for tasks such as image translation, image colorization, and image completion.

Youll also learn about some key findings and implications in the business of DL and about key companies and startups adopting this technology. The book will cover some frameworks for training DL models, key methods, and tricks to fine-tune the models.

The book contains hands-on coding examples, in Keras using Python 3.6.

1.2 Challenges in the Deep Learning Field

Machine learning , and deep learning in particular, is rapidly expanding to almost all business areas. DL is the technology behind well-known applications for speech recognition, image processing, and natural language processing. But some challenges in deep learning remain.

To start with, deep learning algorithms require large data sets. For instance, speech recognition requires data from multiple dialects or demographics. Deep neural networks can have millions or even billion of parameters, and training can be a time-consuming processsometimes weeks in a well-equipped machine.

Hyperparameter optimization (the size of the network, the architecture, the learning rate, etc.) can be a daunting task. DL also requires high-performance hardware for training, with a high-performance GPU and at least 12Gb of memory.

Finally, neural networks are essentially black boxes and are hard to interpret.

1.3 Target Audience

This book was written for academics, data scientists, data engineers, researchers, entrepreneurs, and business developers.

While reading this book, you will learn the following:

What deep learning is and why it is so powerful
What major algorithms are available to train DL models
What the major breakthroughs are in terms of applying DL
What implementations of DL libraries are available and how to run simple examples
Major areas of the impact of DL in business and startups

The book introduces the fundamentals while giving some practical tips to cover the information needed for a hands-on project related to a business application. It also covers the most recent developments in DL from a pragmatic perspective. It cuts through the buzz and offers concrete examples of how to implement DL in your business application.

1.4 Plan and Organization

The book is divided into four parts. Part 1 contains the introduction and fundamental concepts about deep learning and the most important network architectures, from convolutional neural networks (CNNs) to LSTM networks.

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