Videos in this playlist 80 Stanford Seminar - RowHammer, RowPress and Beyond: Can We Be Free of Bitflips (Soon)? Stanford Seminar - Nvidia’s H100 GPU Stanford Seminar - From Designing Quantum Computers to Coordinating the National Quantum Initiative Stanford Seminar - The Impact of Generative AI Systems (An Open Forum Moderated by Dennis Allison) Stanford Seminar - Collaborating with GPT-4: Ken Kahn, Oxford Stanford Seminar - Open Forum Moderated by Dennis Allison of Stanford Stanford Seminar - A Hybrid Password Manager Stanford Seminar - VLSI Industry Trends Stanford Seminar - Should We Pause Giant AI Experiments? Stanford Seminar - How Behavior Spreads Stanford Seminar - Intellectual Property and Artificial Intelligence Stanford Seminar - Using Narrative to Transform Publishing and Business Analytics Stanford Seminar - Remembering Carl Hewitt Stanford Seminar - Fireside Chat between David Farber and Dennis Allison Stanford Seminar - Highly optimized quantum circuits synthesized via data-flow engines Stanford Seminar - Optimizing the Internet Stanford Seminar - Alphy and Alphy Reflect: creating a reflective mirror to advance women Stanford Seminar - Computing with Physical Systems Stanford Seminar - Slow Music in a Manic World: A History of Deep Listening Stanford Seminar: I forgot, I invented hypertext - Ted Nelson Stanford Seminar - Changing the Solar Constant at Earth to a Solar Variable Stanford Seminar - Dataflow for convergence of AI and HPC - GroqChip! Stanford Seminar - Forecasting and Predicting the Future of the Future Stanford Seminar - Making the Invisible Visible: Observing Complex Software Dynamics Stanford Seminar - Accelerating ML Recommendation with over a Thousand RISC-V/Tensor Processors... Stanford Seminar - Digitally Driven Manufacturing: A Profitable Path to a Viable Economy Stanford Seminar - Is Google Search Is Dying? Stanford Seminar - Can We Mitigate Cryptocurrencies’ Externalities Stanford Seminar - We Can Restore the Earth's Climate If We Want to -- And We Should Stanford Seminar - 4004 Microprocessors Stanford Seminar - Universal Intelligent Systems by 2030 - Carl Hewitt and John Perry Stanford Seminar - Preventing Successful Cyberattacks Using Strongly-typed Actors Stanford Seminar - Deep Reckonings: Prosocial uses of deepfake technology Stanford Seminar - Online Political Ad Transparency Stanford Seminar - How to build an IC company Stanford Seminar - How can we understand and evaluate election forecasts, given that N=15 (or less)? Stanford Seminar - PurpleAir - Real time air quality monitoring Stanford Seminar - Computing with FPGAs - Oskar Mencer Stanford Seminar - Coming Attractions: Death or Utopia in the Next Three Decades Stanford Seminar - Practical Blockchain Applications - Steven Pu Stanford Seminar - Computer-designed organisms - Josh Bongard Stanford Seminar - Deep Learning for Symbolic Mathematics - Guillaume Lample & Francois Charton Stanford Seminar - Learning from history: the how and why of starting a computer history museum Stanford Seminar - Building the Smartest and Open Virtual Assistant to Protect Privacy - Monica Lam Stanford Seminar - Rebooting the Internet Stanford Seminar - Data Analytics at the Exascale for Free Electron Lasers Project Stanford Seminar - The Soul of a New Machine: Rethinking the Computer Stanford Seminar - fastai: A Layered API for Deep Learning Stanford Seminar - Centaur Technology's Deep learning Coprocessor Stanford Seminar - KUtrace 2020 Stanford Seminar - Computer Security: The Mess We're In, How We Got Here, and What to Do About It Stanford Seminar - Fight over the Law of Software APIs & stories from Electronic Frontier Foundation Stanford Seminar - Lenia: Biology of Artificial Life, Bert Wang-Chak Chan Stanford Seminar - Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization Stanford Seminar - Locking the Web Open--a Call for a New, Decentralized Web Stanford Seminar - Solving Cybersecurity as an Economic Problem Stanford Seminar - The Current State of Cybersecurity Stanford Seminar - Thunderclap & CHERI (Capability Hardware-Enhanced RISC Instructions) Stanford Seminar - Persistent and Unforgeable Watermarks for DeepNeural Networks Stanford Seminar - Technology Management During a Time of U.S.- China Friction Stanford Seminar - Tales from the Risks Forum Stanford Seminar - Neural Networks on Chip Design from the User Perspective Stanford Seminar - Conflict and Technology Stanford Seminar - A Superscalar Out-of-Order x86 Soft Processor for FPGA Stanford Seminar - SMILE: Synchronized, Multi-sensory Integrated Learning Environment Stanford Seminar - Fingerprinting the Climate System Stanford Seminar - Jupyter Notebooks and Academic Publication Stanford Seminar: Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems Stanford Seminar - MIPS Open, Wave Computing Stanford Seminar - Nanosecond-level Clock Synchronization in a Data Center Stanford Seminar - Deep Learning for Medical Diagnoses Stanford Seminar - Saving energy and increasing density in information processing using photonics Stanford Seminar - Natural Language Processing for Production-Level Conversational Interfaces Stanford Seminar - Extending the theory of ML for human-centric applications Stanford Seminar - Facing up to the byproducts of digitization--with power comes responsibility Stanford Seminar - Training Classifiers with Natural Language Explanations Stanford Seminar - How to Meet Today's Performance, Security, and Affordability Desires Stanford Seminar - Electronic Design Automation and the Resurgence of Chip Design Stanford Seminar - A Borderless Internet with Borderless Laws? Stanford Seminar - Scalable Intelligent Systems Build and Deploy by 2025 Show all 80 Advantages and disadvantages AdvantagesFree and complete: every lecture is in the playlist, in order. Watch at 1.5x, skip what you know, rewatch what you don't. The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere. Taught by Stanford University, one of the strongest names in its field. 371,099 views, so help and notes are easy to find. Completely free. Self-paced: start any time. A clear syllabus (80 videos) you can see before you start. DisadvantagesNo certificate, deadlines or graded work. Quality and depth vary: check that the playlist is finished before you start. Graduate level: needs linear algebra, probability and programming first. No certificate. A big commitment: about 324 videos, 392 hours in all. No graded assignments or feedback. Recorded in 2016: tools may have changed since. Some points apply to every course of this kind; see how we rank .
FreeFree: Watch on YouTube, no account needed.Certificate: None. Code or take notes along to make it stick.