10 Free Resources to Learn LLMs
Learn large language models with these free resources from Deeplearning.AI, Microsoft, AWS, and more.
Large Language Models (LLMs) have emerged as a key topic in AI, and nearly every data-oriented position now necessitates a basic comprehension of these algorithms.
Whether you’re a developer aiming to broaden your skill set, a data professional, or someone intending to shift into the AI sector, gaining knowledge about LLMs can be highly beneficial in today’s job market.
In this article, I will share 10 free resources that will assist you in learning about Large Language Models.
1. Intro to Large Language Models by Andrej Karpathy
For those who are entirely new to AI, I suggest watching this one-hour YouTube tutorial that explains the fundamentals of LLMs.
After watching this video, you’ll gain insights into how LLMs function, the scaling laws associated with LLMs, the process of fine-tuning models, the concept of multimodality, and how to customize LLMs.
2. GenAI for Beginners by Microsoft
Generative AI for Beginners is an 18-lesson program designed to provide you with all the knowledge necessary for developing generative AI applications.
It begins with foundational concepts, introducing you to generative AI and large language models (LLMs) before moving on to subjects such as prompt engineering and how to choose the right LLMs.
Next, you will learn how to create applications powered by LLMs using low-code platforms, retrieval-augmented generation (RAGs), and AI agents.
The course will also cover the process of fine-tuning LLMs and ensuring the security of your LLM applications.
You have the option to skip modules and focus on the lessons that align most closely with your learning objectives.
3. GenAI with LLMs by Deeplearning.AI
This course on Generative AI with LLMs focuses on language models and requires around three weeks of dedicated study.
The study material includes foundational concepts of LLMs, the transformer architecture, and techniques for prompt engineering.
Additionally, you will acquire skills to fine-tune, optimize, and deploy language models using AWS.
4. Hugging Face NLP Course
Hugging Face is a prominent company in the field of NLP that offers libraries and models enabling the development of machine-learning applications. They empower everyday users to create AI applications with ease.
Hugging Face’s NLP curriculum includes a focus on the transformer architecture, the fundamentals of LLMs, and the Datasets and Tokenizer libraries found within their platform.
You will discover how to fine-tune datasets and execute tasks such as text summarization, question answering, and translation utilizing the Transformers library and the Hugging Face pipeline.
5. LLM University by Cohere
LLM University offers a learning platform focusing on NLP and LLM concepts.
Like the earlier courses mentioned, you’ll start with the foundational aspects of LLMs and their architecture before moving on to more sophisticated topics such as prompt engineering, fine-tuning, and RAGs.
If you already have some knowledge of NLP, you can simply skip the basic modules and follow along to the more advanced tutorials.
6. Foundational Generative AI by iNeuron
Foundational Generative AI is a complimentary two-week program that introduces the essentials of generative AI, Langchain, vector databases, open-source language models, and deploying LLMs.
Each module requires about two hours to finish, and it is advised to complete each module within a single day.
Upon completion of this course, you will acquire the skills to create a fully functioning medical chatbot utilizing a language model.
7. Natural Language Processing by Krish Naik
This YouTube playlist on NLP explores topics such as tokenization, text preprocessing, RNNs, and LSTMs.
These subjects are essential for grasping how modern large language models function.
Upon completing this course, you’ll gain insight into the various text-processing methods that are fundamental to NLP.
You’ll also learn about the mechanics of sequential NLP models and the difficulties encountered in their implementation, which ultimately paved the way for the creation of more sophisticated LLMs like the GPT series.
Additional LLM Learning Resources
Some additional resources to learn LLMs include:
1. Papers with Code
Papers with Code is a resource that merges machine learning research articles with corresponding code, simplifying the process of staying updated on the latest advancements in the field along with their real-world applications.
2. Attention is All You Need
For a deeper insight into the transformer architecture, which serves as the backbone for leading language models such as BERT and GPT, I suggest reading the paper entitled “Attention is All You Need”.
This will enhance your comprehension of how large language models function and clarify why transformer-based architectures outperform earlier leading models significantly.
3. LLM-PowerHouse
This GitHub repository collects tutorials, best practices, and code related to LLMs.
It serves as an extensive resource for understanding language models, featuring in-depth discussions of LLM architecture, guides on fine-tuning and deploying models, along with code snippets that can be directly utilized in your own LLM projects.
10 Free Resources to Learn LLMs — Key Takeaways
There is an abundance of resources for learning about LLMs, and I have gathered the most useful ones in this article.
The majority of the educational content referenced here assumes you have some familiarity with coding and machine learning. If you lack experience in these fields, I suggest exploring the following resources:
A Note From the Author
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