Master Large Language Models
In the vast landscape of artificial intelligence, a revolutionary force has emerged: Large Language Models (LLMs). These models are not just buzzwords; they represent the future of AI. Their ability to understand and generate human-like text has catapulted them into the spotlight, making them one of the most exciting and dynamic areas of research today. Imagine a chatbot that responds as naturally as your friends, or a content generation system that produces text so seamlessly that it’s indistinguishable from human writing. If innovations like these captivate your interest and you want to delve deeper into the world of LLMs, you’re in the right place.
To facilitate your journey, I have curated a comprehensive list of resources. This collection includes:
- Online Courses: Access structured learning paths that guide you from beginner to advanced levels. These courses cover the theoretical foundations of LLMs, practical implementations, and hands-on projects. You’ll learn from top instructors and gain the skills needed to build and deploy LLM-based applications.
- Workshops and Conferences: Attend workshops and conferences dedicated to AI and LLMs. These events offer opportunities to learn from experts, network with peers, and discover the latest research and innovations in the field. Keep an eye out for upcoming events to enhance your learning experience.
- Books and Informative Articles: Explore comprehensive texts and Articles authored by leading experts in the field of AI and LLMs. These resources provide in-depth knowledge, case studies, and practical advice on mastering LLMs. Dive into detailed explorations of LLM concepts, applications, and the latest developments in the field. These articles cover a wide range of topics, from the basics of LLMs to their ethical implications and future directions.
- GitHub Repositories: Explore a variety of hands-on projects, code samples, and tools that allow you to experiment with LLMs. These repositories provide practical experience and insights into how LLMs are built and used in real-world scenarios. You can find pre-trained models, fine-tuning scripts, and innovative applications to study and modify.
These resources are designed to help you gain a thorough understanding of LLMs, from the basics to the cutting-edge advancements. Whether you’re a student, a researcher, or an industry professional, this guide will provide you with the knowledge and tools you need to master LLMs.
1. Foundational Courses
- Machine Learning Specialization — Coursera
Link: Machine Learning Specialization
Description: The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
2. Stanford CS229: Machine Learning Course YouTube by Andrew Ng
Link: YouTube Playlist
Description: Listen to the first lecture in Andrew Ng’s machine learning course. This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines.
3. Deep Learning Specialization — Coursera
Link: Deep Learning Specialization
Description: The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
4. Stanford CS224N: NLP with Deep Learning — YouTube
Link: Stanford CS224N: NLP with Deep Learning
Description: It is a goldmine of knowledge and provides a thorough introduction to cutting-edge research in deep learning for NLP. This Course is taught by Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL).
5. HuggingFace Transformers Course — HuggingFace
Link: HuggingFace Transformers Course
Description: This course teaches the NLP by using libraries from the HuggingFace ecosystem. It covers the inner workings and usage of the following libraries from HuggingFace:
- Transformers
- Tokenizers
- Datasets
- Accelerate
About the authors:
Abubakar Abid completed his PhD at Stanford in applied machine learning. During his PhD, he founded Gradio, an open-source Python library that has been used to build over 600,000 machine learning demos. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead.
Matthew Carrigan is a Machine Learning Engineer at Hugging Face. He lives in Dublin, Ireland and previously worked as an ML engineer at Parse.ly and before that as a post-doctoral researcher at Trinity College Dublin. He does not believe we’re going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless.
Lysandre Debut is a Machine Learning Engineer at Hugging Face and has been working on the 🤗 Transformers library since the very early development stages. His aim is to make NLP accessible for everyone by developing tools with a very simple API.
Sylvain Gugger is a Research Engineer at Hugging Face and one of the core maintainers of the 🤗 Transformers library. Previously he was a Research Scientist at fast.ai, and he co-wrote Deep Learning for Coders with fastai and PyTorch with Jeremy Howard. The main focus of his research is on making deep learning more accessible, by designing and improving techniques that allow models to train fast on limited resources.
Dawood Khan is a Machine Learning Engineer at Hugging Face. He’s from NYC and graduated from New York University studying Computer Science. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Gradio was eventually acquired by Hugging Face.
Lewis Tunstall is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O’Reilly book Natural Language Processing with Transformers.
Leandro von Werra is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O’Reilly book Natural Language Processing with Transformers. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack.
6. ChatGPT Prompt Engineering for Developers — Coursera
Link: ChatGPT Prompt Engineering Course
Description: ChatGPT is a popular LLM and this course shares the best practices and the essential principles to write effective prompts for better response generation.
2. LLMs Specific Courses
- LLM University — Cohere
Link: LLM University
Description: Cohere offers a specialized course to master LLMs. Their sequential track, which covers the theoretical aspects of NLP, LLMs, and their architecture in detail, is targeted towards beginners. Their non-sequential path is for experienced individuals interested more in the practical applications and use cases of these powerful models rather than their internal working.
2. Stanford CS324: Large Language Models — Stanford Site
Link: Stanford CS324: Large Language Models
Description: This course delves deeply into the complexities of various models, covering fundamental principles, theoretical frameworks, ethical considerations, and practical applications. Students will gain a comprehensive understanding of these models, exploring their underlying mechanics and broader implications. Additionally, the course offers hands-on experience, allowing participants to apply their knowledge in practical scenarios, thereby bridging the gap between theory and practice.
3. Princeton COS597G: Understanding Large Language Models — Princeton Site
Link: Understanding Large Language Models
Description: It is a graduate-level course that offers a comprehensive curriculum, making it an excellent choice for in-depth learning. You will explore the technical foundations, capabilities, and limitations of models like BERT, GPT, T5 models, mixture-of-expert models, retrieval-based models, etc.
4. Full Stack LLM Bootcamp — The Full Stack
Link: Full Stack LLM Bootcamp
Description: The Full Stack LLM boot camp is a comprehensive, industry-relevant course that equips participants with essential skills for building and deploying LLM applications. The curriculum covers a range of topics including prompt engineering techniques, the fundamentals of LLMs, deployment strategies, and user interface design. By addressing these critical areas, the boot camp ensures that participants gain the knowledge and expertise necessary to successfully develop and implement LLM-based applications in real-world scenarios.
5. ETH Zurich: Large Language Models(LLMs) — RycoLab
Link: ETH Zurich: Large Language Models
Description: This newly designed course offers an in-depth exploration of Large Language Models (LLMs). It delves into the probabilistic foundations that underpin these models, the intricacies of neural network modeling, and the detailed processes involved in training them. Additionally, the course covers scaling techniques essential for handling large datasets and improving model performance. Critical discussions on security and the potential misuse of LLMs are also included, ensuring a well-rounded understanding of both the capabilities and the ethical considerations associated with these advanced technologies.
6. Fine Tuning Large Language Models — Coursera
Link: Fine Tuning Large Language Models
Description: Fine-tuning is a technique that allows you to adapt large language models (LLMs) to meet your specific needs. By completing this course, you will gain an understanding of when to apply fine-tuning, how to prepare data for the process, and the steps involved in training your LLM on new data. Additionally, you will learn how to evaluate the performance of your fine-tuned model, ensuring that it meets your desired objectives and functions effectively in your intended application.
3. Articles / Books
- What Is ChatGPT Doing … and Why Does It Work? — Steven Wolfram
Link: What is ChatGPT Doing … and Why Does It Work?
Description: Steven Wolfram, a renowned scientist, has written a short book that delves into the fundamental aspects of ChatGPT. He explores its origins in neural networks and traces its advancements through transformers, attention mechanisms, and natural language processing. This book is highly recommended for anyone interested in understanding the capabilities and limitations of large language models (LLMs).
2. 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 blend of visualizations, intuitive explanations, and comprehensive coverage of the subject matter.
4. Building LLM Applications for Production — Chip Huyen
Link: Building LLM Applications for Production
Description: In this article, the challenges of productionizing LLMs are discussed. It offers insights into task composability and showcases promising use cases. Anyone interested in practical LLMs will find it really valuable.
4. Github Repositories
1. Awesome-LLM ( 15.7k ⭐ )
Link: Awesome-LLM
Description: It is a curated collection of papers, frameworks, tools, courses, tutorials, and resources focused on large language models (LLMs), with a particular emphasis on ChatGPT.
2. LLMsPracticalGuide ( 8.9k ⭐ )
Link: The Practical Guides for Large Language Models
Description: It helps the practitioners to navigate the expansive landscape of LLMs. It is based on the survey paper titled: Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond and this blog.
3. LLMSurvey ( 9.4k ⭐ )
Link: LLMSurvey
Description: It is a collection of survey papers and resources based on the paper titled: A Survey of Large Language Models. It also contains an illustration of the technical evolution of GPT-series models as well as an evolutionary graph of the research work conducted on LLaMA.
4. Awesome Graph-LLM ( 1.4k⭐ )
Link: Awesome-Graph-LLM
Description: It is a valuable source for people interested in the intersection of graph-based techniques with LLMs. it provides a collection of research papers, datasets, benchmarks, surveys, and tools that delve into this emerging field.
5. Awesome Langchain ( 7k ⭐ )
Link: awesome-langchain
Description: LangChain is the fast and efficient framework for LLM projects and this repository is the hub to track initiatives and projects related to LangChain’s ecosystem.
5. Additional Resources — Research and Survey Papers
- “A Complete Survey on ChatGPT in AIGC Era” — It’s a great starting point for beginners in LLMs. It comprehensively covers the underlying technology, applications, and challenges of ChatGPT.
- “A Survey of Large Language Models” — It covers the recent advances in LLMs specifically in the four major aspects of pre-training, adaptation tuning, utilization, and capacity evaluation.
- “Challenges and Applications of Large Language Models” — Discusses the challenges of LLMs and the successful application areas of LLMs.
- “Attention Is All You Need” — Transformers serve as the foundation stone for GPT and other LLMs and this paper introduces the Transformer architecture.
- “The Annotated Transformer” — A resource from Harvard University that provides a detailed and annotated explanation of the Transformer architecture, which is fundamental to many LLMs.
- “The Illustrated Transformer” — A visual guide that helps you understand the Transformer architecture in depth, making complex concepts more accessible.
- “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” — This paper introduces BERT, a highly influential LLM that sets new benchmarks for numerous Natural Language Processing (NLP) tasks.
