28:48transformers in nlpLecture
LSTM is dead. Long Live Transformers!
Seattle Applied Deep Learning
4 HN recommenders
28:48transformers in nlpLecture
Seattle Applied Deep Learning
4 HN recommenders
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Lex Fridman
6 HN recommenders
27:07
1:30:45automatic differentiationTalk
Microsoft Research
3 HN recommenders
12:47backpropagationExplainer
3Blue1Brown
4 HN recommenders
17:52reward modelling and rlhfExplainer
Robert Miles AI Safety
3 HN recommenders
23:20rigor in machine learningTalk
ObservantVids
3 HN recommenders
17:08neural network game simulationDemo
sentdex
3 HN recommenders
51:46
4:41
47:15AI and neural networksLecture
The Royal Institution
3 HN recommenders
40:37self-driving carsTalk
FunctionalTV
3 HN recommenders
1:11:41natural language processingLecture
Stanford University School of Engineering
3 HN recommenders
13:30machine learning for sign stealingDemo
Mark Rober
3 HN recommenders
42:40
3:07neural networksTutorial
Welch Labs
3 HN recommenders
39:43state space modelsLecture
Sasha Rush
3 HN recommenders
1:02:13
31:32autonomous driving visionTalk
WAD at CVPR
4 HN recommenders
52:52data-driven machine learningTalk
GoogleDeveloperDay
3 HN recommenders
1:02:57data-driven language modelsLecture
UBC Computer Science
3 HN recommenders
1:01:51information theoryLecture
Jakob Foerster
3 HN recommenders
1:11
35:45ai accelerator chipsTalk
The Research and Applied AI Summit - RAAIS
3 HN recommenders
Machine learning involves both the models and the data and systems around them. This collection spans neural networks, language models, computer vision, data engineering and the broader consequences of AI. Students can begin with an introductory topic; practitioners can use the subject links to find a more specific technical discussion.
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