GPT-3 - Helion

ebook

Autor: Sandra Kublik, Shubham Saboo
ISBN: 9781098113582
stron: 150, Format: ebook
Data wydania: 2022-07-11
Księgarnia: Helion

Cena książki: 229,00 zł

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GPT-3: NLP with LLMs is a unique, pragmatic take on Generative Pre-trained Transformer 3, the famous AI language model launched by OpenAI in 2020. This model is capable of tackling a wide array of tasks, like conversation, text completion, and even coding with stunningly good performance. Since its launch, the API has powered a staggering number of applications that have now grown into full-fledged startups generating business value. This book will be a deep dive into what GPT-3 is, why it is important, what it can do, what has already been done with it, how to get access to it, and how one can build a GPT-3 powered product from scratch.

This book is for anyone who wants to understand the scope and nature of GPT-3. The book will evaluate the GPT-3 API from multiple perspectives and discuss the various components of the new, burgeoning economy enabled by GPT-3. This book will look at the influence of GPT-3 on important AI trends like creator economy, no-code, and Artificial General Intelligence and will equip the readers to structure their imaginative ideas and convert them from mere concepts to reality.

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Spis treści

GPT-3 eBook -- spis treści

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