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.
What are the neurons, why are there layers, and what is the math underlying it? Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it’s supposed to in fact be a k. Thanks for the sharp eyes that caught that! There are two neat things about this…
Dec 3, 2024One-day workshop on topics in Generative AI IISc-IBM AI Day is being jointly organized by the Centre for Networked Intelligence (with support from Cisco CSR) and IBM-IISc Hybrid Cloud Lab, in collaboration with IBM India Research Lab. The goal of this workshop would be to apprise the audience of Generative AI, a set…
In a society that is confronting the new age of AI in which LLMs begin to display aspects of human intelligence, understanding the fundamental theory of deep learning and applying it to real systems is a compelling and urgent need. This panel will introduce some new simple foundational results in the theory of supervised learning….
Abstract: In this talk I’ll highlight several exciting trends in the field of AI and machine learning. Through a combination of improved algorithms and major efficiency improvements in ML-specialized hardware, we are now able to build much more capable, general purpose machine learning systems than ever before. As one example of this, I’ll give an…
Topics: Overview of course, OptimizationPercy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford Universityhttp://onlinehub.stanford.edu/ Associate Professor Percy LiangAssociate Professor of Computer Science and Statistics (courtesy) Assistant Professor Dorsa SadighAssistant Professor in the Computer Science Department & Electrical Engineering Department To follow along with the course schedule and syllabus, visit:https://stanford-cs221.github.io/autumn2019/#schedule artificialintelligencecourse 0:00 Introduction3:30 Why…
This video on the Artificial Intelligence tutorial will make you learn in detail about the different concepts involved in AI. You will understand the basics of AI and get an idea about Machine Learning and Deep Learning with hands-on demo in this Artificial Intelligence full course. You will look at how to become an AI…
Oct 9, 2024As LLMs are being integrated into more and more applications, security standards for these integrations have lagged behind. Most security research either focuses 1) on social harms, biases exhibited by LLMs, and other content moderation tasks, or 2) zooms in on the LLM itself and ignores the applications that are built around them….
LLaMA3.2 has released a new set of compact models designed for on-device use cases, such as locally running assistants. Here, we show how LangGraph can enable these types of local assistant by building a multi-step RAG agent – this combines ideas from 3 advanced RAG papers (Adaptive RAG, Corrective RAG, and Self-RAG) into a single…
Get ready for a showdown between LangChain and LangGraph, two powerful frameworks for building applications with large language models (LLMs.) Master Inventor Martin Keen compares the two, taking a look at their unique features, use cases, and how they can help you create innovative, context-aware solutions.
