lecture-notes

A post sharing notes from Professor Zeng Ming's live open class "Viewing the Next Decade," discussing business transformation in the AI era: the shift from product-oriented to consumer-intent-oriented thinking, context organization and learning loops in the Agent era, and how AI-native approaches reshape products, workflows, and organizations with scaled machine intelligence.

Lecture 12 notes from CMU course 11-768 AI Agents are now public, covering Reinforcement Learning Systems: how to train LLM Agents with RL on multi-GPU clusters, coordination between training and inference engines, KV/Prefix Caching, Weight Sync, common pitfalls in Agentic RL (sequence extension, chat templates, TITO, sync vs async), and system design choices ranging from single programs to cloud-native microservices.

This post shares lecture notes for the COS 324 - Introduction to Machine Learning course at Princeton University, providing resources on topics like supervised learning, deep learning, and reinforcement learning.

This paper provides a proof-oriented introduction to diffusion models, covering Langevin dynamics, score-based models, discretization, discrete diffusion, and inference-time control, intended for graduate students.

The article critiques current neural network theory for lacking a governing equation, arguing that AGI remains an extrapolation rather than a well-posed scientific object until learning, inference, and convergence are unified mathematically.

A set of lecture notes covering the mathematics of neural networks, from basic activation functions to geometric concepts like group convolutions and equivariance.

Lecture notes on the foundations of quantum machine learning, covering qubits, superposition, measurement, and the Bloch sphere.

A set of free MIT lecture notes on matrix calculus for machine learning, combining rigorous mathematics with visual explanations.

A structured reinforcement learning course with lecture notes, tutorial tasks, and videos, shared as open-source materials from Paderborn University and University of Siegen.

Summary of David Silver's Reinforcement Learning Lecture 8 on integrating learning and planning, covering model-based RL and AlphaGo's use of policy and value networks with Monte Carlo Tree Search.

Lecture notes from an Efficient AI course covering Transformer and LLM fundamentals, including multi-head attention, positional encoding, KV cache, and the connection between model architecture and inference efficiency. The content explains how design choices in transformers affect memory, latency, and hardware efficiency.