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Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 16: Vision and Language

Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 17: Robot Learning

Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 18: Human-Centered AI

Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 10: Video Understanding

AI in Healthcare Series: Empowering Patients with Kimberly Powell, NVIDIA

How to Make Good Presentations

AI in Healthcare Series: Accelerating the AI Revolution in Medicine, with Peter Lee, Microsoft

Stanford Webinar - Making GenAI Useful: Lessons from Research and Deployment

Stanford Seminar - Cartography to shape morphing at many length scales

Stanford Seminar - Robot Learning Without Action Chunking

Stanford Seminar - Generalization through Task Representations with Foundation Models

Stanford Seminar -The "Embedded Design" Approach to Creating Persuasive Tech & Immersive Experiences

Stanford Seminar - Build less, design more (interactive systems)

Stanford Seminar - What Brains Forgot, Bodies Remember: Building Intelligence from the Ground Up

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 17: Alignment - RL 2

The lecture focuses on reinforcement learning (RL) for language models, specifically delving into policy gradient methods like GRPO. It explains how states, actions, and rewards are defined in this context, emphasizing verifiable outcome rewards. The discussion covers the policy gradient theorem, naive policy gradient,...

AI in Healthcare Series: The Future of Personalized Healthcare Technology with Dr. Jessica Mega

Stanford AA222 I Engineering Design Optimization | Spring 2025 | Multiobjective Optimization

Stanford AA222 I Engineering Design Optimization | Spring 2025 | Disciplined Convex Programming

Stanford CS25: V5 I Transformers for Video Generation, Andrew Brown of Meta

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 16: Alignment - RL

The lecture focuses on reinforcement learning (RL) techniques for language models, specifically contrasting RL from Human Feedback (RLHF) with RL from verifiable rewards. It begins by recapping Direct Preference Optimization (DPO) and its variants, highlighting the challenges of over-optimization and calibration issues...