Image by author | Created with imgflip
Transformers are truly transforming the realm of natural language processing, powering all cutting-edge NLP applications. Google Bard, OpenAI’s ChatGPT, and more: all powered by large (transformer) language models trained on a large corpus of enrichment.
A recent success – OpenAI’s ChatGPT is built on top of the GPT-3 family of large-scale language models. This article provides a list of free courses on large-scale language models to help you develop your technical understanding.
Let’s jump right in!
The CS324: Large Language Models course from Stanford University covers everything you need to know about large language models.
- Capabilities of Large Language Models
- Harms associated with large-scale language models, including toxicity, misinformation, privacy risks, and social bias
- Modeling and training large language models, including encoder-only, decoder-only, and encoder/decoder architectures
- Parallelism
- Scaling and adapting large language models
Course materials and recommended books are available on the course website.
“COS 597G: Understanding Large Language Models” provided by Princeton University is also a free course that covers basic to advanced concepts of large language models. Course materials and recommended books are available on the course website. The syllabus includes:
- Basics of large language models, but a detailed view of t5 GPT 3
- In-Depth Review of BERT, T5 and GPT 3
- Facilitate the language model Facilitate the language model Model
- Scaling and Risk of Large Language Models
- Search-based language model
- multimodal language model
Large Language Models from Rycolab at ETH Zurich is a new course running now (Spring 2023). This course will officially start on his February 21, 2023, and the lecture slides and recommended books will be updated gradually on the course website. This course will help you learn:
- Stochastic basis Stochastic basis
- Basics of modeling
- Neural network modeling and inference
- Training, Fine-tuning, Inference
- Parallelism and scale-up
- security and abuse
CS224n: Deep Learning for NLP, taught by Professor Chris Manning of Stanford University, is a must-take for anyone interested in natural language processing. From traditional NLP and linguistics concepts to large-scale language models and ethical issues, this course provides a comprehensive and solid foundation in the field of natural language processing.
Winter 2021 and Spring 2022 lectures are available on YouTube.
If you want to learn all about Transformers and start building your own NLP applications for natural language inference, summarization, question answering and more, look no further than the free HuggingFace Transformers course.
It consists of three sections to help you understand the HuggingFace ecosystem.
- Using the HuggingFace Transformer
- Library of datasets and tokenizers
- Building production-ready NLP applications
So far, we’ve covered free courses on large language models. Next, we’ll take a look at other helpful resources that are readily available.
Jay Alammar’s article series on large language models
From illustrated transformers to applying large-scale language models to the real world with Cohere, Jay Alammar’s technical blog is one of the best resources for understanding the details of natural language processing.
Understanding Language Models at Scale – The Transformative Reading List
Sebastian Raschka has compiled “Understanding Large Language Model – A Transformative Reading List” of his research on large language models. This reading list will help you understand his progress in the NLP field over the years, from his pre-translator RNN to his Google BERT to his ChatGPT today.
Lang chain
LangChain is a Python library that helps build useful applications on top of large language models. Examples include question answering using domain-specific corpora, training agents to solve specific problems, etc.
For information on setting up your development environment, getting started, and an API reference, see our documentation.
Here is a LangChain demo by Harrison Chase, creator of LangChain.
We hope you found this roundup of resources on large language models helpful. We provide a combination of courses, reading lists, other useful resources, and frameworks to help you build your own powerful LLM-based applications.
If you want to learn more about how ChatGPT works, check out this list of free resources to learn ChatGPT.
Bala Priya C is a technical writer with a hobby of creating long-form content. Her areas of interest include mathematics, programming and data her science. She creates tutorials, how-to guides, and more to share what she’s learned with the developer community.
