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.
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
This is the last of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples. 00:00 Introduction01:50 What is a transformer?04:35 Generating one word at a time08:59 Sentiment Analysis13:05 Neural Networks18:18 Tokenization19:12 Embeddings25:06 Positional encoding27:54 Attention32:29 Softmax35:48 Architecture of a Transformer39:00 Fine-tuning42:20 Conclusion
For more information about Stanford’s Artificial Intelligence programs visit: https://stanford.io/ai https://www.youtube.com/watch?v=Bl4Feh_Mjvo To follow along with the course, visit:https://cs229.stanford.edu/syllabus-s… Tengyu MaAssistant Professor of Computer Sciencehttps://ai.stanford.edu/~tengyuma/ Christopher RéAssociate Professor of Computer Sciencehttps://cs.stanford.edu/~chrismre/
Ready to launch your vector search game? 🚀 Ditch your traditional keywords and discover the power of vector search! This video will help you discover ways users can make search smarter, and generate creative text along the way. Get hands-on with vector search on Vertex AI! Jump directly to the topics you want to learn:00:00…
Generative AI Agents represent the current frontier of LLM technology, enabling dynamic interactions and intelligent workflow automation. However, the complexities of architecting and deploying these agents can be daunting. In this live session, Patrick Marlow demystifies the process, guiding you through the critical decisions and trade-offs involved in building production-ready agents. Explore the full spectrum…
Generative AI Agents represent the current frontier of LLM technology, enabling dynamic interactions and intelligent workflow automation. However, the complexities of architecting and deploying these agents can be daunting. In this live session, Patrick Marlow demystifies the process, guiding you through the critical decisions and trade-offs involved in building production-ready agents. Explore the full spectrum…
Check out how large language models (LLMs) and generative AI intersect to push the boundaries of possibility. Unlock real-world use cases and learn how the power of a prompt can enhance LLM performance. You’ll also explore Google tools to help you learn to develop your own gen AI apps. https://www.youtube.com/watch?v=RBzXsQHjptQ https://www.youtube.com/watch?v=RBzXsQHjptQ
What is generative AI? Not sure where to begin? Check out this short course! We’ll take you through the basics of generative AI including common applications, model types, and the fundamentals of how you can use it. Discover the power behind this technology and see how it’s changing the way we solve problems, and tell…
This is CS50, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming. TABLE OF CONTENTS 00:00:00 – Welcome00:01:01 – Introduction00:03:13 – Image Generation00:08:23 – ChatGPT00:11:06 – Prompt Engineering00:12:40 – CS50.ai00:19:03 – Generative AI00:22:08 – Decision Trees00:26:33 – Minimax00:34:27 – Machine Learning00:42:56 – Deep Learning00:48:53 – Large Language Models00:53:36 –…
