CMU Advanced NLP Spring 2025 (16): Parallelism and Scaling
This lecture (by Sean Welleck) for CMU CS 11-711, Advanced NLP covers: Basics of training on one GPU Parallelization on multiple GPUs (e.g., data, tensor, pipeline parallel) Combining and comparing strategies Content (including figures) based on The Ultra-Scale Playbook: https://huggingface.co/spaces/nanotro...

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CMU Advanced NLP Spring 2025 (17): Long-Context Models

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Lecture 48: The Ultra Scale Playbook
![Yann LeCun's $1B Bet Against LLMs [Part 1]](https://i.ytimg.com/vi/kYkIdXwW2AE/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLDbV4izF3i-wxevCVIn7FJjoy1vlA)
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Yann LeCun's $1B Bet Against LLMs [Part 1]

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Designing Data-intensive Applications with Martin Kleppmann

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OWASP's Top 10 Ways to Attack LLMs: AI Vulnerabilities Exposed

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Emergent Complexity

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CMU Advanced NLP Spring 2025 (2): Neural Text Representation and Classification

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AlphaFold - The Most Useful Thing AI Has Ever Done

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Visualizing transformers and attention | Talk for TNG Big Tech Day '24

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Demis Hassabis: We're Three Quarters of the Way to AGI

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CMU Advanced NLP Spring 2025 (11): Reinforcement Learning

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Why I Left Quantum Computing Research

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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

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Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)

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How AI Cracked the Protein Folding Code and Won a Nobel Prize

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Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

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CMU Advanced NLP Fall 2025 (22): Test-Time Scaling Strategies

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CMU Advanced NLP Spring 2025 (9): Fine-tuning

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CMU Advanced NLP Fall 2024 (6): Instruction Tuning

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