Cognitive Computing with IBM Watson

图书信息

作者	Rob High
出版社	Packt Publishing
ISBN	9781788478984
出版时间	2019-04-30
字数	25.9万
分类	进口书,外文原版书,电脑,网络

读书简介

Understand, design, and create cognitive applications using Watson’s suite of APIs. Key Features * Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps * Learn how to build smart apps to carry out different sets of activities using real-world use cases * Get well versed with the best practices of IBM Watson and implement them in your daily work Book Description Cognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows. This book introduces readers to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM Watson APIs. From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic," as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the concept in their respective fields. What you will learn * Get well versed with the APIs provided by IBM Watson on IBM Cloud * Learn ML, AI, cognitive computing, and neural network principles * Implement smart applications in fields such as healthcare, entertainment, security, and more * Understand unstructured content using cognitive metadata with the help of Natural Language Understanding * Use Watson’s APIs to create real-life applications to realize their capabilities * Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is for This book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.

目录

About Packt

Why subscribe?

Contributors

About the authors

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Background, Transition, and the Future of Computing

Transitioning from conventional to cognitive computing

Limitations of conventional computing

Solving conventional computing's problems

Workings of machine learning

Machine learning and its uses

Cons of machine learning

Introduction to IBM Watson

Hardware and software requirements

Signing up for IBM Cloud

Summary

Can Machines Converse Like Humans?

Creating a conversational agent workspace

Creating an instance of Watson Assistant and a workspace

The sample application

Creating a set of conversational intents

Recognizing entities

Identifying entities through annotators

Building a dialog

Creating the dialog for a complex Intent using Frame Slots

Context variables

Programming your conversation application

Emerging features

Summary

Further reading

Computer Vision

Can machines visually perceive the world around them?

The past – classical computer vision

The present – deep learning for computer vision

Creating a basic image-recognition system

Creating an instance of Watson Visual Recognition and a classifier

Uploading data and training the classifier

Testing the classifier

Creating a Python application to classify with Watson

Handling the case where you don't have training data

Using the facial detection model

Summary

This Is How Computers Speak

A computer that talks

Playing sound through the speaker

Getting fancier with how to speak

Controlling pronunciation

Customizing speech synthesis

Using sounds-like customization

Streaming and timing

A fun application of the speech service

Talking to the computer

Getting voice from a microphone

Using the WebSockets interface to speech recognition

Telephones are not good microphones

More about base models

Dealing with speaker hesitations

Customizing the speech recognition service

Customizing Watson's language model

Customizing the acoustic model for Watson

Leveraging batch processing

Summary

Further reading

Expecting Empathy from Dumb Computers

Introducing empathy

Understanding the complexities of sentiment

The functionality of the Tone Analyzer API

How you can use the Tone Analyzer API

Understanding personality through natural language

Using natural language to infer personality traits

Calling the Personality Insights API

Summary

Language - How Watson Deals with NL

Natural language translation – the past

Natural language – it's intrinsically unstructured

Natural language translation – the present

Translating between languages with Language Translator

Training custom NMT models with Watson

Categorizing text using Natural Language Classifier

Summary

Further reading

Structuring Unstructured Content Through Watson

Using computers that recognize what you mean

Introducing the NLU service

Alternative sources of literature

Types of analyses

Categories

Concepts

Emotion

Sentiment

Entities

Relations

Keywords

Semantic roles

Parts of speech (syntax)

Customizing NLU

Preparing to annotate

Creating a type system

Adding documents

As an aside

Preparing documents for use in Watson Knowledge Studio

Loading documents into Watson Studio

Performing annotations

Editing the type system

The importance of being thorough

Coreferences

Training Watson

Deploying the custom model to NLU

Using a custom model in NLU

Summary

Putting It All Together with Watson

Recapping Watson Services

Building a sample application from Watson Services

The use case and application

The program flow

Translating voice input

Determining intent

Prompting the user for their input

Setting the document of interest

Summarizing entities and concepts

Identifying an entity of interest

Assessing the personality of the entity

Assessing the tone of the entity

Translating text

Classifying text

Running the program

Setup

Summary

Future - Cognitive Computing and You

Other services and features of Watson

Compare and Comply

Discovery

Watson Studio

Machine learning

Knowledge catalog

Watson OpenScale

The future of Watson

Advances in AI

Generative adversarial networks

Conversational systems

Deep learning

Edge computing

Bias and ethics in AI

Robotics and embodiment

Quantum computing and AI

The future of AI

Summary

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