Advanced NLP: Introduction to GPT

Natural Language Processing 2022    |    Intermediate
  • 12 Videos | 1h 9m 49s
  • Includes Assessment
  • Earns a Badge
Generative Pre-trained Transformer (GPT) models go beyond classifying and predicting text behavior to helping actually generate text. Imagine an algorithm that can produce articles, songs, books, or code - anything that humans can write. That is what GPT can help you achieve. In this course, discover the key concepts of language models for text generation and the primary features of GPT models. Next, focus on GPT-3 architecture. Then, explore few-shot learning and industry use cases and challenges for GPT. Finally, practice decoding methods with greedy search, beam search, and basic and advanced sampling methods. Upon completing this course, you will understand the fundamentals of the GPT model and how it enables text generation.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    outline key concepts of language models
    outline key features of GPT models
    illustrate various GPT versions
    define GPT-3 model architecture
    define few-shot learning as used in GPT-3
  • outline industry use cases and challenges for GPT
    demonstrating text generation with GPT - perform library installation, use tokenizer, and download the GPT model
    demonstrating text generation with GPT - perform greedy and beam searches and use basic sampling
    demonstrating text generation with GPT - perform Top K and Top P sampling
    demonstrating text generation with GPT - use benchmark prompts to perform model generations given interesting inputs
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 17s
    UP NEXT
  • Playable
    2. 
    Language Models
    6m 10s
  • Locked
    3. 
    Generative Pre-trained Transformer (GPT)
    4m 36s
  • Locked
    4. 
    GPT Versions
    9m 48s
  • Locked
    5. 
    GPT-3 Model Architecture
    6m 18s
  • Locked
    6. 
    GPT-3 Few-Shot Learning
    1m 30s
  • Locked
    7. 
    GPT-3 Use Cases and Challenges
    5m 49s
  • Locked
    8. 
    Downloading the GPT Model
    7m 28s
  • Locked
    9. 
    Performing Greedy and Beam Searches in GPT
    14m 51s
  • Locked
    10. 
    Performing Top K and Top P Sampling in GPT
    6m 26s
  • Locked
    11. 
    Using Benchmark Prompts in GPT
    4m 45s
  • Locked
    12. 
    Course Summary
    51s

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