Stanford Online

Stanford Seminar - Learning to control large teams of robots

Stanford CS25: V5 I Transformers in Diffusion Models for Image Generation and Beyond

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 15: Alignment - SFT/RLHF

The lecture focuses on post-training techniques for large language models, transitioning from pre-training to making models useful and safe. It covers two main areas: supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). The discussion includes the importance of high-quality data, the chal...

Stanford Seminar - Hardware / controls co-design to overcome challenges for aerial robots

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 14: Data 2

This lecture focuses on data filtering and deduplication techniques used in training language models. It begins by outlining filtering algorithms, including N-gram models, FastText classifiers, and importance resampling, emphasizing speed and generalization. The lecture then discusses how these filtering methods can be...

Stanford CS25: V5 I Multimodal World Models for Drug Discovery, Eshed Margalit of Noetik.ai

Stanford Seminar - Beyond Words: Affordances of Embodiment in Automated Health Counselors

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 13: Data 1

The lecture focuses on the importance of data in training language models, arguing it's the most critical factor. It covers the various stages of training, including pre-training, mid-training, and post-training, and the types of data used in each. The lecture explores specific datasets like BERT, WebText, Common Crawl...

Stanford CS25: V5 I On the Biology of a Large Language Model, Josh Batson of Anthropic

Stanford Seminar - Multitask Transfer in TRI’s Large Behavior Models for Dexterous Manipulation

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 12: Evaluation

The podcast discusses the complexities and challenges of evaluating language models, highlighting the "evaluation crisis" due to saturated or gamed benchmarks. It covers various evaluation methods, including benchmark scores (MMLU, AIME, etc.), cost analysis, user choice data, and human preferences. The speaker emphasi...

Stanford Seminar - Evaluating and Improving Steerability of Generalist Robot Policies

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 11: Scaling laws 2

In this lecture, the speaker delves into scaling laws for large language models, emphasizing their practical application in model building. The discussion covers case studies of models like Cerebrus GPT, Mini-CPM, and DeepSeq, highlighting their unique scaling strategies and lessons learned. A significant portion of th...

AI, Longevity, and the Future of Healthcare: A Conversation with Dr. Eric Topol

AI is fundamentally reshaping healthcare, yet medical institutions remain slow to integrate these technologies into curricula and clinical practice. Medical education must shift from prioritizing rote memorization to fostering humanistic qualities, as AI now demonstrates unexpected proficiency in empathy and diagnostic...

Stanford Webinar - Managing Team Performance

Stanford Seminar - A tentative mea culpa - looking back to help the next gen of CSCW researchers

Stanford CS25: V5 I Large Language Model Reasoning, Denny Zhou of Google Deepmind

Denny Zhou from Google DeepMind delivers a lecture on large language model reasoning, defining reasoning as the intermediate tokens between input and output. He discusses the importance of these intermediate tokens, referencing theoretical work and examples like the last letter concatenation task. Zhou challenges the b...

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 10: Inference

Lecture 10 focuses on inference in the context of language models, distinguishing it from training by highlighting its memory-limited nature and the challenges of generating tokens sequentially. The lecture covers key inference metrics like time to first token, latency, and throughput, and explains how these are affect...

Program Overview: Modern Construction

Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 9: Scaling laws 1

The podcast focuses on scaling laws in machine learning, particularly for large language models (LLMs). It begins by framing the challenge of building the best open-source LLM with limited resources, emphasizing the need to innovate rather than just copy existing models. The discussion covers the history and background...