Hands-on guides and insights and research for self-taught minds exploring the depths of ML.
This section is for the builders who learn by doing — the coders who don’t wait for permission, the late-night tinkerers turning tutorials into breakthroughs. Whether you’re retraining your first model or tuning your fiftieth, this category is designed for autodidacts ready to go deeper.
We focus on applied knowledge, not academic gatekeeping. You’ll find approachable explorations of topics like:
Fine-tuning language and vision models
Building and analyzing datasets
Experimenting with embeddings and graph-based methods
Practical Reinforcement Learning
Retrieval-Augmented Generation workflows
Toolkits for structured and multi-modal generation
All written with clarity, precision, and respect for those learning outside formal institutions.
Here at n0c, we honor curiosity as much as credentials, and we believe you should have access to advanced concepts without a degree or a gatekeeper. If you’re ready to go beyond tutorials and into deeper understanding, this section is for you.
LangChain became immensely popular when it was launched in 2022, but how can it impact your development and application of AI models, Large Language Models (LLM) in particular. In this video Martin Keen shares an overview of the features and uses of LangChain.
What is Generative AI and how does it work? What are common applications for Generative AI? Watch this video to learn all about Generative AI, including common applications, model types, and the fundamentals for how to use it.
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer. This Python course teaches you how to use RAG to combine your own custom data with the power of Large Language Models (LLMs). 💻 Code: https://github.com/langchain-ai/rag-from-scratch ⭐️ Course Contents ⭐️⌨️ (0:00:00) Overview⌨️ (0:05:53) Indexing⌨️ (0:10:40) Retrieval⌨️ (0:15:52) Generation⌨️ (0:22:14)…
Demystifying attention, the key mechanism inside transformers and LLMs.
To follow along with the course, visit the course website:https://deepgenerativemodels.github.io/ Stefano ErmonAssociate Professor of Computer Science, Stanford Universityhttps://cs.stanford.edu/~ermon/ https://www.youtube.com/watch?v=XZ0PMRWXBEU
This is the 5th video in a series on using large language models (LLMs) in practice. Here, I discuss how to fine-tune an existing LLM for a particular use case and walk through a concrete example with Python code.
Learn prompt engineering techniques to get better results from ChatGPT and other LLMs.
This is the second of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples. 00:00 Introduction01:18 Recap: Embeddings and Context04:46 Similarity11:09 Attention20:46 The Keys and Queries Matrices25:02 The Values Matrix28:41 Self and Multi-head attention33:54: Conclusion
Attention mechanisms are crucial to the huge boom LLMs have recently had.In this video you’ll see a friendly pictorial explanation of how attention mechanisms work in Large Language Models.This is the first of a series of three videos on Transformer models. https://www.youtube.com/watch?v=OxCpWwDCDFQ
