Image generated with Leonardo.ai
In this vast ai landscape, a revolutionary force has emerged in the form of large language models (LLMS). It is not just a buzzword but our future. Their ability to understand and generate human-like texts put them in the spotlight and has now become one of the hottest areas of research. Imagine a chatbot that can respond to you as if you were talking to your friends, or imagine a content generation system that is difficult to distinguish if it is written by a human or an ai. If things like this intrigue you and you want to delve deeper into the heart of LLMs, then you’re in the right place. I’ve put together a comprehensive list of resources ranging from informative articles, courses, and GitHub repositories to relevant research articles that can help you understand them better. Without further delay, let us begin our amazing journey in the world of LLMs.
Image by Polina Tankilevitch on Pexels
1. Deep Learning Specialization – Coursera
Link: Specialization in deep learning
Description: Deep learning forms the backbone of LLMs. This comprehensive course taught by Andrew Ng covers the essential topics of neural networks, the basics of computer vision and natural language processing, and how to structure your machine learning projects.
2. Stanford CS224N: NLP with Deep Learning – YouTube
Link: Stanford CS224N: NLP with Deep Learning
Description: It is a goldmine of knowledge and provides a comprehensive introduction to cutting-edge research in deep learning for NLP.
3. HuggingFace Transformers Course – HuggingFace
Link: HuggingFace Transformers Course
Description: This course teaches NLP using libraries from the HuggingFace ecosystem. Covers the inner workings and usage of the following HuggingFace libraries:
- transformers
- Tokenizers
- Data sets
- Speed up
4. ChatGPT Message Engineering for Developers – Coursera
Link: ai/short-courses/chatgpt-prompt-engineering-for-developers/” rel=”noopener” target=”_blank”>ChatGPT Rapid Engineering Course
Description: ChatGPT is a popular LLM and this course shares best practices and essential principles for writing effective prompts to generate a better response.
Image generated with Leonardo.ai
1. LLM University – Cohere
Link: LLM University
Description: Cohere offers a specialized course to master LLMs. Its sequential track, which covers in detail the theoretical aspects of NLP, LLMs and their architecture, is aimed at beginners. Their non-sequential path is for experienced people interested more in the practical applications and use cases of these powerful models than their inner workings.
2. Stanford CS324: Large Language Models – Stanford Site
Link: Stanford CS324: Large Language Models
Description: This course delves into the complexities of these models. You will explore the foundations, theory, ethics and practicalities of these models while gaining practical experience.
3. Princeton COS597G: Understanding Large Language Models – Princeton Site
Link: Understand large language models
Description: It is a postgraduate course that offers a comprehensive curriculum, making it an excellent option for in-depth learning. You will explore the technical foundations, capabilities and limitations of models such as BERT, GPT, T5 models, expert matching models, recovery-based models, etc.
4. eth Zurich: Large Language Models (LLM) – RycoLab
Link: eth Zurich: large language models
Description: This newly designed course offers a comprehensive exploration of LLMs. Delve into probabilistic foundations, neural network modeling, training processes, escalation techniques, and critical discussions around security and potential misuse.
5. Full Stack LLM Bootcamp – The Full Stack
Link: Full Stack LLM Intensive Course
Description: The Full Stack LLM Bootcamp is an industry-relevant course that covers topics such as rapid engineering techniques, LLM fundamentals, deployment strategies, and user interface design, ensuring participants are well prepared to build and deploy LLM applications.
6. Fine Tuning Large Language Models – Coursera
Link: ai/short-courses/finetuning-large-language-models/” rel=”noopener” target=”_blank”>Fine tuning large language models
Description: Fine Tuning is the technique that allows you to adapt LLMs to your specific needs. Upon completion of this course, you will understand when to apply adjustment, preparing data for adjustment, and how to train your LLM with new data and evaluate its performance.
Image generated with Leonardo.ai
1. What does ChatGPT do… and why does it work? -Steven Wolfram
Link: What does ChatGPT do… and why does it work?
Description: This short book is written by Steven Wolfram, a renowned scientist. He discusses the fundamentals of ChatGPT, its origins in neural networks, and its advances in transformers, attention mechanisms, and natural language processing. It is an excellent read for someone interested in exploring the capabilities and limitations of LLMs.
2. Understanding Large Language Models: A Transformative Reading List – Sebastian Raschka
Link: Understanding Large Language Models: A Transformative Reading List
Description: It contains a collection of important research articles and provides a chronological reading list, from the early papers on recurrent neural networks (RNN) to the influential BERT model and beyond. It is an invaluable resource for researchers and practitioners to study the evolution of NLP and LLMs.
3. Article Series: Large Language Models – Jay Alammar
Link: Article series: Large language models
Description: Jay Alammar’s blogs are a treasure trove of knowledge for anyone studying large language models (LLMs) and transformers. His blogs stand out for their unique combination of visualizations, intuitive explanations, and comprehensive topic coverage.
4. Building LLM Applications for Production: Chip Huyen
Link: Building LLM applications for production
Description: This article discusses the challenges of producing LLMs. Provides insight into task composability and shows promising use cases. Anyone interested in practical LLMs will find it really valuable.
Image by RealToughCandy.com on Pexels
1. Awesome-LLM (9k )
Link: Awesome-LLM
Description: It is a curated collection of articles, frameworks, tools, courses, tutorials, and resources focused on large language models (LLM), with a special emphasis on ChatGPT.
2. LLM Practical Guide (6.9k )
Link: The Field Guides to Large Language Models
Description: Helps professionals navigate the broad landscape of LLMs. It is based on the survey document titled: Harnessing the Power of LLMs in Practice: A Survey of ChatGPT and Beyond and this Blog.
3. LLM Survey (6.1k )
Link: LLM Survey
Description: It is a collection of survey articles and resources based on the article titled: A survey of large language models. It also contains an illustration of the technical evolution of the GPT series models, as well as an evolutionary graph of the research work carried out at LLaMA.
4. Awesome Graph-LLM (637 )
Link: Awesome-Graph-LLM
Description: It is a valuable source for people interested in the intersection of graph-based techniques with LLM. provides a collection of research articles, data sets, benchmarks, surveys, and tools that delve deeper into this emerging field.
5. Awesome Langchain (5.4k )
Link: awesome-langchain
Description: LangChain is the fast and efficient framework for LLM projects and this repository is the hub for tracking initiatives and projects related to the LangChain ecosystem.
- “A comprehensive survey on ChatGPT in the AIGC era” – It is a great starting point for beginners in LLM. It comprehensively covers the underlying technology, applications, and challenges of ChatGPT.
- “A survey of large language models” – Covers recent advances in LLMs specifically in the four main aspects of pre-training, adaptive adjustment, utilization, and capability assessment.
- “Challenges and applications of large language models”- Analyzes the challenges of LLMs and the successful application areas of LLMs.
- “Attention is all you need” – Transformers serve as a cornerstone for GPT and other LLMs and this paper presents the Transformer architecture.
- “The annotated transformer” – A resource from Harvard University that provides a detailed and annotated explanation of the Transformer architecture, which is central to many LLMs.
- “The illustrated transformer” – A visual guide that helps you understand the Transformer architecture in depth, making complex concepts more accessible.
- “BERT: Deep Bidirectional Transformer Pre-Training for Language Understanding” – This article introduces BERT, a highly influential LLM that sets new benchmarks for numerous natural language processing (NLP) tasks.
In this article, I have curated an extensive list of essential resources for mastering large language models (LLM). However, learning is a dynamic process and knowledge sharing is essential. If you have additional resources in mind that you think should be part of this comprehensive list, feel free to share them in the comments section. Your contributions could be invaluable to others on their learning journey, creating an interactive and collaborative space for knowledge enrichment.
Kanwal Mehreen is an aspiring software developer with a strong interest in data science and ai applications in medicine. Kanwal was selected as a Google Generation Scholar 2022 for the APAC region. Kanwal loves sharing technical knowledge by writing articles on trending topics and is passionate about improving the representation of women in the tech industry.
