Covers: theory of Language Modelling
Estimated time needed to finish: 23 minutes
Questions this item addresses:
  • What is Language Modelling?
How to use this item?

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Author(s) / creator(s) / reference(s)
Jason Brownlee
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GPT-3

Contributors
Total time needed: ~2 hours
Objectives
Understanding the characteristics and capabilities of GPT-3 and differences with the previous transformer based language models.
Potential Use Cases
natural language generation, summarization, question answering, classification
Who is This For ?
INTERMEDIATE
Click on each of the following annotated items to see details.
PAPER 1. Understanding the GPT-3
  • What is GPT-3 ?
  • How is GPT-3 different from previous transformer based architectures?
  • How GPT-3 uses Few shot learning and zero shot learning to eliminate fine-tuning and the need for large task specific datasets?
25 minutes
PAPER 2. Transformers
  • What are transformers ?
25 minutes
ARTICLE 3. Introduction to Language Modelling
  • What is Language Modelling?
23 minutes
PAPER 4. Few Shot Learning
  • What is few shot learning?
10 minutes
ARTICLE 5. Approaches and Applications of Few Shot Learning
  • Where is few shot learning used?
15 minutes

Concepts Covered

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