I very much love those "let's series": let's build GPT{model, tokenizer}, let's reproduce GPT-2… Thanks for the efforts and relentless will to educate the world, Andrej
@jeande_d
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Graduation from Carnegie Mellon’s Master’s in Engineering AI
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Life updates: Excited to share that I have graduated from Masters of Science in Engineering AI at Carnegie Mellon. It's been a hell of ride and I am very grateful to have been part of the program over the past two years. I have got the chance to work with truly amazing people on
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Kaiming He’s Deep Residual Learning Impact on AI
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Deep Residual Learning and How It Shaped the AI Landscape – Kaiming He Finally got to watch the talk of Kaiming He, the lead of some of the most influential works in computer vision such as ResNet, Mask R-CNN, MAE(masked autoencoders), and contributor to other works that shaped
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Stanford CS25 Transformers United V3 New Lectures Released
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[New lectures ] Transformers United – Stanford CS25 The new lectures of Transformers United class V3 have been realized(gradually). Current lectures are about foundational models and generalist agents. Lecture videos: https://
youtube.com/playlist?list=
PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM
…
Website: https://
web.stanford.edu/class/cs25/ -
Review of The Little Book of Deep Learning by François Fleuret
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Received a copy of "The Little Book of Deep Learning" by François Fleuret. This is one of the most concise deep learning books on the web. It is "a lossless compression of all important knowledge" about neural network techniques & models. A pocket book basically. More broadly,
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State of AI Report 2023: Comprehensive Overview of AI Developments
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The State of AI Report 2023 was released a few days ago. The report is one-stop center of all interesting things that happened last year(prior to release): developments and trends in AI research, industry, politics & safety, and future predictions. https://
stateof.ai -
Understanding Precision and Recall in Machine Learning
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I wrote a post on how to think about precision and recall a while back. But like everyone, this is something I always have to loop-up. "Precision: What is the percentage of positive predictions that are actually positive? Recall: What is the percentage of actual positives that
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The Little Book of Deep Learning: A Concise Guide to Deep Learning Fundamentals
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The Little Book of Deep Learning, François Fleuret, University of Geneva Arguably one of the most concise deep learning books on the web. Covers a range of topics from fundamentals, efficient computation, training deep models, architectures, applications, and generative tasks.
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Comprehensive Guide to Large Language Models Technical Details
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Large Language Models (in 2023) Excellent talk on LLMs that highlights important technical details related to LLMs in <50 minutes. From scaling, pre-training to post-training techniques such as instruction tuning and reward modelling. This is a recommended watch for everyone
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High-Quality LLM Watch and Reading List with Transformer Blueprint
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A high-quality LLMs watch/reading list here: https://
gist.github.com/rain-1/eebd5e5
eb2784feecf450324e3341c8d
… Contains videos/lectures/articles that do great job at explaining LLMs and GPT models including our "Transformer Blueprint" article 🙂