Stanford Online

Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)

Andrew Ng: Opportunities in AI - 2023

Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)

Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors

Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng

Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduction - Emma Brunskill

Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors

Overview Artificial Intelligence Course | Stanford CS221: Learn AI (Autumn 2019)

Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)

Stanford Engineering Hero Lecture: Morris Chang in conversation with President John L. Hennessy

Jen-Hsun Huang: Stanford student and Entrepreneur, co-founder and CEO of NVIDIA

Stanford Webinar: Visual Computing-Tracking the Top Trends and Opportunities

What does the future hold for Natural Language Processing? - Andrew Ng & Chris Manning

Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 - Deep Learning Intuition

Stanford Seminar - Nvidia’s H100 GPU

Natural Language Inference | Stanford CS224U Natural Language Understanding | Spring 2021

Bayesian Networks 9 - EM Algorithm | Stanford CS221: AI (Autumn 2021)

Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.2 - Training Graph Neural Networks

Stanford CS105: Introduction to Computers | 2021 | Lecture 18.1 Additional Python Language Features

Building Business Models - Online Course Overview

Stanford CS105 | 2021 | Lecture 7.3 Intro to HTML: Creating a Webpage Step-by-Step

Stanford Course - Genetic Engineering & Biotechnology

Stanford Webinar: Designing Your Life - How to Build a Well-Lived, Joyful Life

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Stanford CS229 Machine Learning I Naive Bayes, Laplace Smoothing I 2022 I Lecture 6

Stanford Seminar - Dynamic Code Optimization and the NVIDIA Denver Processor

Stanford Seminar - Persistent and Unforgeable Watermarks for DeepNeural Networks

Stanford Seminar - I ♥ Logs: Apache Kafka, Stream Processing, and Real-time Data

Stanford EE274: Data Compression I 2023 I Lecture 9 - Context-based AC & LLM Compression

Stanford Webinar - CRISPR - 10 Years of Genome Editing and More

Stanford Seminar - Designing Crowdsourcing Techniques Based on Expert Creative Practice

Stanford Online Course - Network Security

Stanford Seminar - Bridging AI & HCI: Incorporating Human Values into the Development of AI Tech

Stanford CS224N NLP with Deep Learning | 2023 | Lecture 8 - Self-Attention and Transformers

Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention

Stanford Webinar - Design Thinking = Method, Not Magic, Bill Burnnett

Stanford CS105: Intro to Computers | 2021 | Lecture 6.2 Network Protocols: Protocols of the Internet

Stanford CS25: V3 I Low-level Embodied Intelligence w/ Foundation Models

NLU and Information Retrieval | Stanford CS224U Natural Language Understanding | Spring 2021

Stanford CS105: Introduction to Computers | 2021 | Lecture 21.1 Computer Security (Attacks): Malware

Stanford Seminar - Decentralized Finance (DeFi)

Stanford Seminar - Making Videos Accessible - Amy Pavel

Stanford Seminar - Towards trusted human-centric robot autonomy

Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019

Stanford CS224N NLP with Deep Learning | 2023 | Lecture 16 - Multimodal Deep Learning, Douwe Kiela

Stanford CS330 Deep Multi-Task & Meta Learning - Multi-Task Learning Basics I 2022 I Lecture 2

Stanford Seminar - Crowds, networks, and data as catalysts for interactive systems

Stanford Seminar - Deep Learning in Speech Recognition

Learner Spotlight: Andrew Pelosi

Stanford CS25: V2 I Represent part-whole hierarchies in a neural network, Geoff Hinton

Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares

Stanford Seminar - Systems for Supporting Intent Formation and Human-AI Communication

Statistical Learning: 6.9 Dimension Reduction Methods

Stanford Seminar - Emerging Trends and Applications of Light Field Displays

Stanford CS25: V3 I Generalist Agents in Open-Ended Worlds

Stanford Seminar - I❤️ LA: Compilable Markdown for Linear Algebra

Stanford Webinar - Completing projects faster and smarter with Virtual Design and Construction

Stanford Seminar - Nanosecond-level Clock Synchronization in a Data Center

Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)