In this video, we start a new series where we explore the first 5 items in the reading that Ilya Sutskever, former OpenAI chief scientist, gave to John Carmack. Ilya followed by saying that "If you really learn all of these, you’ll know 90% of what matters today".
References
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Transformer Self-Attention Mechanism Explained: • Transformer Self-Attention Mechanism ...
Long Short-Term Memory (LSTM) Equations Explained: • Long Short-Term Memory (LSTM) Equatio...
Reading List
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The Annotated Transformer: https://nlp.seas.harvard.edu/annotate...
The First Law of Complexodynamics: https://scottaaronson.blog/?p=762
The Unreasonable Effectiveness of Recurrent Neural Networks: https://karpathy.github.io/2015/05/21...
Understanding LSTM Networks: https://colah.github.io/posts/2015-08...
Recurrent Neural Networks Regularization: https://arxiv.org/pdf/1409.2329
Related Videos
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Why Language Models Hallucinate: • Why Language Models Hallucinate
Grounding DINO, Open-Set Object Detection: • Object Detection Part 8: Grounding DI...
Transformer Self-Attention Mechanism Explained: • Transformer Self-Attention Mechanism ...
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Multi-Head Attention (MHA), Multi-Query Attention (MQA), Grouped Query Attention (GQA) Explained: • Multi-Head Attention (MHA), Multi-Que...
LLM Prompt Engineering with Random Sampling: Temperature, Top-k, Top-p: • LLM Prompt Engineering with Random Sa...
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits: • The Era of 1-bit LLMs: All Large Lang...
Contents
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00:00 - Intro
00:59 - Item 1: The Annotated Transformer
01:29 - Item 2: The First Law of Complexodynamics
02:21 - Item 3: The Unreasonable Effectiveness of Recurrent Neural Networks
03:05 - Item 4: Understanding LSTM Networks
03:36 - Item 5: Recurrent Neural Networks Regularization
04:11 - Outro
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Watch video AI Reading List (by Ilya Sutskever) - Part 1 online without registration, duration hours minute second in high quality. This video was added by user DataMListic 08 June 2024, don't forget to share it with your friends and acquaintances, it has been viewed on our site 16,823 once and liked it 648 people.