MIT Flow Matching and Diffusion Lecture 2026 Released

Recordings have just dropped on YouTube! You should definitely check out this resource if you want to learn about flow matching. Peter is the best of the best and he put so much effort into this. It was a pleasure to contribute my small part:)

If you'd like to learn more about flow matching and diffusion modeling, check out this amazing course by Peter Holderrieth. #flowmatching #diffusionmodels

Just found this MIT course on diffusion models looks really useful. Highly recommended for anyone interested in modern generative AI.

Unarguably one of the best resources to learn diffusion and flow matching models — combining rigorous theory with hands-on coding for modern generative AI. Kuddos to Peter Holderrieth and team! 🎉

I really enjoyed the Diffusion model IAP class at MIT this year. The course made complex concepts easy to understand, connecting theory with practical applications, and helped me build a more solid understanding of generative AI. I highly recommend this class to anyone exploring generative models. #MIT #GenerativeAI #DiffusionModels #MachineLearning #AI #DeepLearning

My recent 7 articles: - KV Cache in LLMs - Paged Attention in LLMs - Causal Masking in Attention - Byte Pair Encoding in LLMs - Harness Engineering in AI - Math behind Attention - Q, K, and V - Math behind √dₖ Scaling Factor in Attention Read here: https://lnkd.in/g_fghvsK #ai #llm #machinelearning #math

The most underrated skill in AI isn't coding. It's mathematical thinking. Here's why: Every breakthrough in AI was a mathematical insight first. Attention mechanism? Linear algebra. Backpropagation? Chain rule from calculus. GANs? Game theory. Diffusion models? Stochastic differential equations. The engineers who understand the math don't just use AI tools. They build them. If you're learning AI in 2026, don't skip the math. It's not the slow path. It's the only path that compounds. #AI #Mathematics #DeepLearning #MachineLearning

The Department of Artificial Intelligence & Machine Learning, in collaboration with the AI Club, organized a hands-on session on “Implementation of an AI Agent – Level 1” on 1st April 2026. The session was conducted by Mr. Premnath Kini under the guidance of Mr. Arjun K, introducing students to the fundamentals of intelligent agents, including perception, decision-making, and rule-based actions. Participants gained hands-on experience in building simple AI agents using basic programming techniques. A total of 16 students participated, making the session interactive and insightful, while strengthening their foundation in AI concepts. #cec #canaraengineeringcollege #aiml #aiclub #artificialintelligence #aiagents #machinelearning #techskills #skilldevelopment #studentlearning #handsonlearning #campusevents

Understanding feature representation starts with a bit of math but don’t worry, nothing too scary. In this video, Miss Paws breaks down dimensions, scalars, and vectors, and explain why the terminology you learned in math class doesn’t always match what we use in machine learning. From 0‑D points to 3‑D volumes, and from simple numbers to full vector representations, we explore how features map onto dimensions. We also look at how vectors are drawn, how to interpret column vectors, and why it’s crucial not to mix up axes with data points. These fundamentals form the backbone of feature representation, especially as models scale into hundreds or thousands of dimensions. This is Video 3 of Module 2: Introduction to Machine Learning - Feature Engineering in the MehtA+ AI/Machine Learning: Foundations to Frontiers course. Adapted from the popular university-level MehtA+ AI/ML Research Bootcamp for high school students, this course is now freely available to learners everywhere. #ai #machinelearning #features #dimensions #scalars #vectors #math https://lnkd.in/ghNfyiXS

📈 Day 0️⃣2️⃣ : Mathematics - The Hidden Engine of AI ✅ A lot of people quit AI because they are scared of Math. But here is a secret: You don’t need to be a human calculator! In AI, the computer does the math -you just need to understand the concepts. Think of Mathematics as the Engine of a car. You don’t need to build the engine from scratch, but you must know how it works to drive the car to its destination. ▶️ Continue Reading from Medium Article : https://lnkd.in/gRhspSch #AI #100DaysOfCode #MathForAI #DataScience #Calculus #LinearAlgebra #MachineLearning