10 Free Resources to Learn LLMs

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In a previous article, I explained how AI is the skill of the future, with functions that command pay up to$ 375, 000 annually.

Large English Designs ( LLMs) have become a hot topic in AI, and nearly every data-centric part now necessitates a basic understanding of these techniques.

You stand to gain a lot from learning about LLMs in the current employment market, whether you’re a designer looking to increase your skill set, a statistics specialist, or a professional looking to move into the field of AI.

I’ll give you ten free resources to learn about Big Language Designs in this article.

1. Andrej Karpathy’s Intro to Huge Language Models

I suggest starting with this one-hour-long YouTube article explaining the processes used by LLMs if you are a total beginner.

By the end of this video, you may understand the workings behind LLMs, LLM weighting laws, type fine-tuning, diagnostic, and LLM flexibility.

2. GenAI for Beginners by Microsoft

An 18-lesson program called Generative AI for Beginners will teach you all you need to know about creating relational AI applications.

You will first be introduced to the concepts of relational AI and LLMs, and then move on to matters like quick architecture and LLM choice.

Next, you will learn to create LLM-powered programs using low-code equipment, RAGs, and AI agencies.

The program will even teach you how to fine-tune LLMs and protected your Bachelor software.

You are free to skip components and pick the classes that most closely match your learning objectives.

3. GenAI with LLMs by Deeplearning. AI

A course on terminology designs, Generative AI with LLMs, will take you three weeks of full-time research.

This teaching tool covers the basics of LLMs, converter infrastructure, and rapid architectural.

You will also learn to fine-tune, optimize, and deploy language models on AWS.

4. Hugging Face NLP Course

Hugging Face is a leading NLP company that offers libraries and models that make it simple to create machine-learning applications. They make it simple for common users to create AI applications.

Hugging Face’s NLP learning track covers the transformer architecture, the workings behind LLMs, and the Datasets and Tokenizer libraries available within their ecosystem.

You will learn to fine-tune datasets and perform tasks like text summarization, question-answering, and translation using the Transformers library and Hugging Face’s pipeline.

5. LLM University by Cohere

LLM University is a learning resource that covers NLP and LLM topics.

Similar to the previous courses on this list, you will begin by learning about the basics of LLMs and their architecture, and progress to more advanced concepts like prompt engineering, fine-tuning, and RAGs.

You can skip the fundamental modules and continue to the more in-depth tutorials if you already have some knowledge of NLP.

6. iNeuron’s foundational genealogical AI

Foundational Generative AI is a free 2-week course that covers the basics of generative AI, Langchain, vector databases, open-source language models, and LLM deployment.

It is recommended that each module be finished in one day because each module typically takes two hours to complete.

By the end of this course, you will learn to implement an end-to-end medical chatbot using a language model.

7. Krish Naik’s Natural Language Processing

This NLP playlist on YouTube covers concepts like tokenization, text preprocessing, RNNS, and LSTMs.

These topics must be covered in order to comprehend how large language models currently operate.

You will be able to identify the various text-processing methods that make up NLP after taking this course.

You will also learn the principles underlying the sequential NLP models and the difficulties in putting them into practice, which ultimately resulted in the creation of more sophisticated LLMs like the GPT series.

Additional LLM Learning Resources

Among the additional sources for learning LLMs are:

1. Papers with Code

A platform called Papers with Code combines ML research papers with code, making it simpler for you to stay informed about the most recent developments in the field alongside real-world applications.

2. All You Need is Attention, That’s All.

I suggest reading the research paper titled” Attention is All You Need” to learn more about the transformer architecture, which is the foundation of modern language models like BERT and GPT.

This will give you a better understanding of how LLMs work and why transformer-based models perform significantly better than previous state-of-the-art models.

3. LLM-PowerHouse

This is a GitHub repository that curates LLM tutorials, best practices, and code.

It is a comprehensive guide to language model — with detailed explanations of LLM architecture, tutorials on model fine-tuning and deployment, and code snippets that can be used directly in your own LLM applications.