Recipe to Learn GPT-3: the Most Powerful Language Model

A brief guide to GPT-3 and its related concepts such as Language Modeling, Transformers, and Few Shot Learning

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For a data scientist, learning and upskilling are essential skills. We know that keeping up to date with the exponentially growing field of data science requires special learning skills. There are so many resources available to learn the same topics. It’s difficult to select and keep track of these learning resources. I was using bookmarks but when I needed to link different topics, it became always challenging to locate them based on tags. A few months back, I found a learning recipe tool from the aggregate intellect that made it easy to keep track of my learning by creating recipes. I have been crafting learning recipes using the recipe tool for the past four months which helped me to accelerate my learning journey and share my learning resources with others.

Staying up to date with Data Science whilst filtering gold from impurities is imperative for any Data Scientist who is serious about upskilling their craft.

I have created more than ten recipes to learn many topics in natural language processing. Recipes come in handy when I need to explain concepts to someone or refer to them. Easy way to share information and help others to learn the concept with my learning recipe. Furthermore, I have referred to some awesome recipes created by others that I found to be time-saving. Recently, I was planning to study GPT-3 (Generative Pre-trained Transformer 3). The following learning recipe helped me to learn this topic.

This post was originally published as a recipe on https://ai.science. See this recipe for an interactive version of this post and to comment or collaborate.

What’s Covered?

Nausheen Fatma believes that the following resources and instructions are the best way to understand the characteristics and capabilities of GPT-3 and differences with the previous transformer-based language models.

What is GPT-3?

This post will provide a brief guide to GPT-3 and its related concepts Language Modeling, Transformers, and Few shot learning. I’ll also point you to the resources available for further reading and exploration.

Who is learning the recipe for?

Anyone with an intermediate understanding of GPT-3 can use their learning from this post to tackle use cases like natural language generation, summarization, question answering, and classification.

1. Use this asset, Introduction to Language Modelling, to answer questions like “What is Language Modelling?”.
2. Use this asset to learn about Transformers to answer questions like “What are transformers ?” I recommend reading sections 3, 4, 5, and 7.
3. Use this asset, Understanding the GPT-3 to answer questions like “What is GPT-3 ?”, “How is GPT-3 different from previous transformer-based architectures?”, and “How GPT-3 uses few-shot learning and zero-shot learning to eliminate fine-tuning and the need for large task-specific datasets?”.
4. Use Few Shot Learning to answer questions like “What is few-shot learning?”. I recommend reading sections 2, 3, and 4.
5. Use Approaches and Applications of Few Shot Learning to answer questions like “Where is few-shot learning used?”.

Summary :

GPT-3 learning recipe takes approximately 2 hours to complete. It helped me to learn quickly without spending time locating other resources. Transformer family, attention, and large language model concepts were easily reviewed along with learning about the latest, most powerful language model GPT-3. Hope you enjoy learning by creating learning recipes.