Spelled-Out Intro to Neural Networks & Backpropagation: Building microgradNeural Networks: Zero to Hero · Lec 1·Neural Networks & Backpropagation · Andrej Karpathy · Independent / Eureka LabsAndrej KarpathyIndependent / Eureka Labs5 ch2:34:45
Recurrent Neural Networks, Transformers, and AttentionAI & ML·Standalone Lecture·Language Models & NLP · Ava Amini · MITAva AminiMIT12 ch1:02:50
Introduction to Deep Learning6.7960 · Deep Learning · Lec 1·Neural Networks & Backpropagation · Sara Beery · MITSara BeeryMIT—1:00:51
The Deep Learning Revolution (Turing Award Lecture)AI & ML·Public Colloquium·Mechanistic Interpretability, Alignment & World Models · Geoffrey Hinton · Association for Computing Machinery (ACM)Geoffrey HintonAssociation for Computing Machinery (ACM)—32:28
Digital vs. Biological Intelligence (Vienna Gödel Lecture)AI & ML·Public Colloquium·Language Models & NLP · Geoffrey Hinton · University of TorontoGeoffrey HintonUniversity of Toronto6 ch1:20:05
The Deep Learning Revolution: The Sequel (Turing Award Lecture)AI & ML·Public Colloquium·Computer Vision & Graphics · Yann LeCun · Association for Computing Machinery (ACM)Yann LeCunAssociation for Computing Machinery (ACM)—37:56
Learning Representations: A Challenge for Learning Theory, COLT 2013AI & ML·Conference Talk·Statistical Learning & Causality · Yann LeCun · New York UniversityYann LeCunNew York University—54:33
Neural Networks I: Feedforward Architectures & BackpropagationAI & ML·Standalone Lecture·Statistical Learning & Causality · Chris Ré · Stanford UniversityChris RéStanford University—1:20:28
Objective-Driven AI: World Models that Learn, Remember, Reason, and PlanAI & ML·Research Seminar·Language Models & NLP · Yann LeCun · Harvard UniversityYann LeCunHarvard University—1:16:53
Computing with High-Dimensional VectorsAI & ML·Research Seminar·Deep Learning · Pentti Kanerva · Stanford UniversityPentti KanervaStanford University—59:10
Governance at the Technological Frontier: Translating AI Research into Oversight PolicyAI & ML·Research Seminar·Deep Learning · Inioluwa Deborah Raji · UC BerkeleyInioluwa Deborah RajiUC Berkeley—39:55
How to Train a Neural Net6.7960 · Deep Learning · Lec 2·Mechanistic Interpretability, Alignment & World Models · Sara Beery · MITSara BeeryMIT—1:19:34
Introduction to Convolutional Neural Networks for Visual RecognitionConvolutional Neural Networks for Visual Recognition · Lec 1·Computer Vision & Graphics · Fei-Fei Li, Justin Johnson & Serena Yeung · Stanford UniversityFei-Fei Li&Justin JohnsonStanford University—57:56
Innate Priors in Deep Learning SystemsAI & ML·Research Seminar·Language Models & NLP · Yann LeCun & Christopher Manning · Stanford UniversityYann LeCun&Christopher ManningStanford University—1:02:56
Reflecting on Neural ODEs at NeurIPS 2019AI & ML·Conference Talk·Calculus & Analysis · David Duvenaud · University of TorontoDavid DuvenaudUniversity of Toronto—21:02
Turing Lecture: Deep Learning for AI and System-2 ReasoningAI & ML·Conference Talk·Language Models & NLP · Yoshua Bengio · Association for Computing Machinery (ACM)Yoshua BengioAssociation for Computing Machinery (ACM)—44:57
Improving Physical Realism in AI Weather ForecastsAI & ML·Research Seminar·Neural Networks & Backpropagation · Scott Hosking · Alan Turing InstituteScott HoskingAlan Turing Institute19 ch36:32
How to Build a Brain: An Introduction to Neuromorphic ComputingBio & Neuro·Public Colloquium·Deep Learning · Ghent University (VVN)VVGhent University (VVN)—1:12:23
Harvard CS50’s Artificial Intelligence with Python – Full University CourseAI & ML·Standalone Lecture·Deep Learning · Brian Yu · Harvard UniversityBrian YuHarvard University—11:51:22
Approximation Theory6.7960 · Deep Learning · Lec 3·Neural Networks & Backpropagation · Jeremy Bernstein · MITJeremy BernsteinMIT—1:22:42