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Cognitive Computing with IBM Watson
Cognitivecomputingisrapidlyinfusingeveryaspectofourlivesridingonthreeimportantfields:datascience,machinelearning(ML),andartificialintelligence(AI).Itallowscomputingsystemstolearnandkeeponimprovingastheamountofdatainthesystemgrows.Thisbookintroducesreaderstoawholenewparadigmofcomputing–aparadigmthatistotallydifferentfromtheconventionalcomputingoftheInformationAge.YouwilllearntheconceptsofML,deeplearning(DL),neuralnetworks,andAIthroughthesetofAPIsprovidedbyIBMWatson.Thisbookwillhelpyoubuildyourownapplicationstounderstand,plan,andsolveproblems,andanalyzethemasperyourneeds.Youwilllearnaboutvariousdomainsofcognitivecomputing,suchasNLP,voiceprocessing,computervision,emotionanalytics,andconversationalsystems,usingdifferentIBMWatsonAPIs.Fromthis,thereaderwilllearnwhatMLis,andwhatgoesoninthebackgroundtomakecomputers"dotheirmagic,"aswellaswheretheseconceptshavebeenapplied.Havingachievedthis,thereaderswillthenbeabletoembarkontheirjourneyoflearning,researching,andapplyingtheconceptintheirrespectivefields.
目錄(163章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Cognitive Computing with IBM Watson
- 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
- Another Book You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 15:03:06
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