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AI & Cybersecurity: Dan Boneh Interviews Nicolas Carlini

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 4 - LLM Training

The lecture focuses on the training of Large Language Models (LLMs), contrasting traditional task-specific training with transfer learning. It covers pre-training LLMs on vast datasets to predict the next token, using datasets like Common Crawl, and introduces metrics like FLOPs to measure compute. The discussion highl...

Stanford CS230 | Autumn 2025 | Lecture 4: Adversarial Robustness and Generative Models

In this lecture, Kian Katanforoosh explores two main topics: adversarial robustness and generative modeling. The discussion on adversarial robustness covers attacks on AI models, including prompt injection and data poisoning, and the importance of building proactive defenses. Katanforoosh outlines three waves of advers...

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 2 - Transformer-Based Models & Tricks

The lecture provides an overview of self-attention mechanisms, transformer architecture, and position embeddings. It begins with a recap of self-attention, emphasizing queries, keys, and values, and the transformer's encoder-decoder structure for machine translation. The discussion covers learned versus static position...

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 1 - Transformer

Afshine Amidi and Shervine introduce CME 295, a Stanford course on Transformers and Large Language Models (LLMs), outlining their backgrounds and the course's evolution from a workshop. They detail the course's objectives, which include understanding the underlying mechanisms of LLMs and their applications, and prerequ...

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 3 - Tranformers & Large Language Models

The lecture introduces Large Language Models (LLMs), defining them as large-scale language models predicting token sequences, emphasizing their size in parameters, training data, and computational needs. It distinguishes LLMs from earlier models like BERT, highlighting the decoder-only architecture and introduces Mixtu...

Stanford Robotics Seminar ENGR319 | Autumn 2025 | Make Every Step an Experiment

Stanford Global Alumni Webinar | August 2025 | AI Agent Simulation of Human Behavior

Stanford CS230 | Autumn 2025 | Lecture 3: Full Cycle of a DL project

Andrew Ng delivers a lecture about the full cycle of a deep learning project, emphasizing the iterative nature of machine learning development due to the unpredictable nature of data. He uses the example of building a face recognition system to illustrate the different stages, including problem specification, data coll...

Stanford CS547 HCI Seminar | Autumn 2025 | HCI Systems in the Age of AI Code Generation

AI & Cybersecurity: Neil Daswani Interviews Heather Adkins

Stanford CS230 | Autumn 2025 | Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning

Kian Katanforoosh leads an interactive lecture structured in multiple parts, starting with a recap of the week's online learning, including neurons, layers, and deep neural networks. He then transitions into supervised learning projects like day and night classification, trigger word detection, and face verification, e...

Stanford Webinar - Building Human-Centered AI: From Reward Functions to Real Products

Stanford CS230 | Autumn 2025 | Lecture 1: Introduction to Deep Learning

In this Q&A podcast, a speaker provides an overview of the CS230 deep learning course at Stanford, explaining its flipped classroom format, where students watch lectures online and class time is used for discussions. The speaker contextualizes deep learning within computer science and AI, highlighting its effectiveness...

Stanford Webinar - From Pixels to Human Impact: Unlocking Real-World Value with Computer Vision

Stanford CS229 I Machine Learning I Building Agents That Do the Work of Human Software Engineers

The podcast explores the construction and utilization of multi-agent systems for software engineering, emphasizing a balance between simplicity and complexity. It begins with basic LLM calls and progresses to multi-agent systems, assessing the problem-solving capabilities at each stage, using a payment system failure a...

AI In Healthcare Series: Leveraging GPT-5, Cosmos, and Predictive Models for Better Outcomes

Stanford CS329H: Machine Learning from Human Preferences | Autumn 2024 | Voting

Stanford CS329H: Machine Learning from Human Preferences | Autumn 2024 | Ethics

Stanford CS329H: Machine Learning from Human Preferences | Autumn 2024 | Human-centered Design