Advanced NLP: Introduction to GPT

Natural Language Processing    |    Intermediate
  • 12 videos | 1h 9m 49s
  • Includes Assessment
  • Earns a Badge
Rating 3.6 of 50 users Rating 3.6 of 50 users (50)
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

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