MIT 6.S191 (2025): Large Language Models (Liquid AI)
MIT Introduction to Deep Learning 6.S191: Lecture 8 Large Language Models Lecturer: Maxime Labonne (Liquid AI) Maxime Labonne is the Head of Post-Training at Liquid AI. He has made significant contributions to the open-source community, including the LLM Course, tutorials on fine-tuning, tools such as LLM AutoEval, and several state-of-the-art models like NeuralDaredevil. He is the author of the best-selling books “LLM Engineer’s Handbook” and “Hands-On Graph Neural Networks Using Python”. For all lectures, slides, and lab materials: http://introtodeeplearning.com Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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MIT 6.S191 (2025): A Hipocratic Oath, for *your* AI (Comet ML)

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MIT 6.S191: Secrets of Massively Parallel Training

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MIT 6.S191: AI for Science

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Yann LeCun's $1B Bet Against LLMs

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Transformers, the tech behind LLMs | Deep Learning Chapter 5

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MIT 6.S191 (2024): Reinforcement Learning

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RAG vs. CAG: Solving Knowledge Gaps in AI Models

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MIT 6.S191: The Three Laws of AI

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Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

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Visualizing transformers and attention | Talk for TNG Big Tech Day '24
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[Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han

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Most devs don't understand how LLM tokens work

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MIT 6.S191 (2025): Large Language Models (Google)

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MIT 6.S191: Convolutional Neural Networks

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MIT 6.S191 (2024): Convolutional Neural Networks

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LLMs Don't Need More Parameters. They Need Loops.

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Everything you need to know about Fine-tuning and Merging LLMs: Maxime Labonne

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MIT 6.S191 (2025): Convolutional Neural Networks

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Don't learn AI Agents without Learning these Fundamentals

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