GPT-3

Spis treści książki

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • From Sandra
    • From Shubham
• 1. The Era of Large Language Models
  • Natural Language Processing: Under the Hood
  • Language Models: Bigger and Better
  • The Generative Pre-Trained Transformer: GPT-3
    • Generative Models
    • Pre-trained Models
    • Transformer Models
      • Sequence-to-sequence models
      • Transformer attention mechanisms
  • A Brief History of GPT-3
    • GPT-1
    • GPT-2
    • GPT-3
  • Accessing the OpenAI API
• 2. Using the OpenAI API
  • Navigating the OpenAI Playground
    • Prompt Engineering and Design
  • How the OpenAI API Works
    • Execution Engine
    • Response Length
    • Temperature and Top P
    • Frequency and Presence Penalties
    • Best Of
    • Stop Sequence
    • Inject Start Text and Inject Restart Text
    • Show Probabilities
  • Execution Engines
    • Davinci
    • Curie
    • Babbage
    • Ada
    • Instruct Series
  • Endpoints
    • List Engines
    • Retrieve Engine
    • Completions
    • Semantic Search
    • Files
    • Classification (Beta)
    • Answers (Beta)
    • Embeddings
  • Customizing GPT-3
    • Apps Powered by Customized GPT-3 Models
    • How to Customize GPT-3 for Your Application
      • Prepare and upload training data
      • Train a new fine-tuned model
      • Use the fine-tuned model
  • Tokens
  • Pricing
  • GPT-3s Performance on Conventional NLP Tasks
    • Text Classification
      • Zero-shot classification
      • Single-shot and few-shot classification
      • Batch classification
    • Named Entity Recognition
    • Text Summarization
    • Text Generation
      • Article generation
      • Social media post generation
  • Conclusion
• 3. Programming with GPT-3
  • Using the OpenAI API with Python
  • Using the OpenAI API with Go
  • Using the OpenAI API with Java
  • GPT-3 Sandbox Powered by Streamlit
  • Going Live with GPT-3-Powered Applications
  • Conclusion
• 4. GPT-3 as a Launchpad for Next-Generation Start-ups
  • Model-as-a-Service
  • The New Start-up Ecosystem: Case Studies
    • Creative Applications of GPT-3: Fable Studio
    • Data Analysis Applications of GPT-3: Viable
    • Chatbot Applications of GPT-3: Quickchat
    • Marketing Applications of GPT-3: Copysmith
    • Coding Applications of GPT-3: Stenography
  • An Investors Perspective on the GPT-3 Start-up Ecosystem
  • Conclusion
• 5. GPT-3 for Corporations
  • Case Study: GitHub Copilot
    • How It Works
    • Developing Copilot
    • No-Code/Low-Code: Simplifying Software Development?
    • Scaling with the API
    • Whats Next for GitHub Copilot?
  • Case Study: Algolia Answers
    • Evaluating NLP Options
    • Data Privacy
    • Cost
    • Speed and Latency
    • Lessons Learned
  • Case Study: Microsoft Azure OpenAI Service
    • A Partnership That Was Meant to Be
    • An Azure-Native OpenAI API
    • Resource Management
    • Security and Data Privacy
    • Model-as-a-Service at the Enterprise Level
    • Other Microsoft AI and ML Services
    • Advice for Enterprises
    • OpenAI or Azure OpenAI Service: Which Should You Use?
  • Conclusion
• 6. Challenges, Controversies, and Shortcomings
  • The Challenge of AI Bias
    • Anti-Bias Countermeasures
  • Low-Quality Content and the Spread of Misinformation
  • The Environmental Impact of LLMs
  • Proceeding with Caution
  • Conclusion
• 7. Democratizing Access to AI
  • No Code? No Problem!
  • Access and Model-as-a-Service
  • Conclusion
• Index

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