Understand, design, and create cognitive applications using Watson's suite of APIs Key Features Work with IBM Watson APIs to build efficient and powerful cognitive apps Build smart apps to carry out different sets of activities with the help of 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 becoming a part of every aspect of our lives through 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 increases. This book introduces you 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 with the help of IBM Watson APIs. This book will help you build your own applications to understand, and solve problems, and analyze them as per your needs. You will explore various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems. Equipped with the knowledge of machine learning concepts, how computers do their magic, and the applications of these concepts, you'll be able to research and apply cognitive computing in your projects. What you will learn Get well-versed with the APIs provided by IBM Watson on IBM Cloud Understand ML, AI, cognitive computing, and neural network principles Implement smart applications in fields such as healthcare, entertainment, security, and more Explore unstructured data using cognitive metadata with the help of Natural Language Understanding Discover the capabilities of IBM Watson's APIs by using them to create real-life applications Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is for If you're new to cognitive computing, you'll find this book useful. Although not a prerequisite, some knowledge of artificial intelligence and deep learning will be an added advantage. This book covers these concepts using IBM Watson's tools. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/Cognitive-Computing-with-IBM-Watson . If you require support please email: customercare@packt.com.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Chapter 1: 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 
• Chapter 2: 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
• Chapter 3: 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
• Chapter 4: 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 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
• Chapter 5: 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
• Chapter 6: 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
• Chapter 7: 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
• Chapter 8: 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
• Chapter 9: Future - Cognitive Computing and You
  • Other services and features of Watson
    • Compare and Comply (C&C)
    • Discovery
    • Watson Studio
    • Machine learning
    • Knowledge catalog
    • Watson OpenScale
  • The future of Watson
  • Advances in AI
    • Generative adversarial networks
    • Conversational systems
    • Deep learning (DL)
    • Edge computing
    • Bias and ethics in AI
    • Robotics and embodiment
    • Quantum computing and AI
  • The future of AI
  • Summary 
• Another Book You May Enjoy
• Index