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Lecture 4: Transformers and Large Pretrained Models (Sweta Agrawal)
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Lecture 6: Vision and Language (Desmond Elliott)
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What Is Machine Learning? (The Clearest Explanation You'll Find)
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Lecture 3: Introduction to Sequence Models (Noah Smith)
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Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)
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Local AI voice cloning
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Lecture 5: Causality (Adèle Ribeiro)
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But what is cross-entropy? | Compression is Intelligence Part 2
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Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker
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You’ll stop using ChatGPT after listening to this | Jonathan Pageau [ARC 2026]
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Talk: Rethinking Test-Time Scaling Laws (Beidi Chen)
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MIT Professor: Leetcode, P vs NP, SAT Solvers | Ryan Williams
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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
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The Scariest Chart in Electrical Engineering
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Talk: Reality Checks (Kyunghyun Cho)
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Mathe-News 🚨 KI löst das Erdős-Einheitsabstand-Problem!
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The Riskiest Moment of the AI Bubble
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How To Think SO Clearly People Assume You're Brilliant
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Lecture 4: Transformers and Large Pretrained Models (Danqi Chen)
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