@amneumarkt · Post #687 · 18.07.2025, 06:24
#ml https://aryagxr.com/blogs/intro-to-nmc
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Источник @procode404 · Post #3885 · 18 дек.
Почему развитие в ИИ стоит начинать с изучения математики и алгоритмов Руководитель Школы анализа данных Яндекса в подкасте Machine Learning Podcast рассказывает, почему фундамент (матан, линал, теорвер, алгоритмы) — это не скучная теория, а база для работы с ИИ в 2026. Вы узнаете, как глубокое понимание математики помогает писать эффективный код, отлаживать модели и ориентироваться в разных областях ML. А ещё — почему даже опытным разработчикам полезно возвращаться к фундаментальным дисциплинам. Перейти к прослушиванию #подкаст#ML
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@amneumarkt · Post #687 · 18.07.2025, 06:24
#ml https://aryagxr.com/blogs/intro-to-nmc
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@amneumarkt · Post #682 · 27.06.2025, 05:30
#ml Machine Learning Visualized — Machine Learning Visualized https://ml-visualized.com/?utm_source=substack&utm_medium=email
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@amneumarkt · Post #617 · 25.08.2024, 14:03
#ml What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/
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@amneumarkt · Post #607 · 30.07.2024, 06:20
#ml Meta's second version of segment anything. https://github.com/facebookresearch/segment-anything-2 They have a nice demo: https://sam2.metademolab.com/
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@amneumarkt · Post #596 · 07.07.2024, 20:53
#ml I was searching for a tool to visualize computational graphs and ran into this preprint. The hierarchical visualization idea is quite nice. https://arxiv.org/abs/2212.10774
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@amneumarkt · Post #595 · 06.07.2024, 22:02
#ml Schmidhuber J. Deep Learning: Our Miraculous Year 1990-1991. In: arXiv.org [Internet]. 12 May 2020 [cited 7 Jul 2024]. Available: https://arxiv.org/abs/2005.05744
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@amneumarkt · Post #575 · 09.05.2024, 19:51
#ml https://github.com/google-research/timesfm
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@amneumarkt · Post #538 · 16.02.2024, 11:21
#ml Like a dictionary Kunc, Vladim’ir, and Jivr’i Kl’ema. 2024. “Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.09092.
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@amneumarkt · Post #532 · 09.02.2024, 05:35
#ml I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions. Then I saw this paper today: Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
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@amneumarkt · Post #531 · 05.02.2024, 10:57
#ml Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032 Not surprising at all when you have direct access to a long context. But hey, look at this title.
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@amneumarkt · Post #523 · 13.01.2024, 19:43
#ml Interesting idea to use Hydra in ML experiments. https://github.com/ashleve/lightning-hydra-template
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@amneumarkt · Post #490 · 23.06.2023, 11:53
#ml Hand-Crafted Transformers HandCrafted.ipynb - Colaboratory https://colab.research.google.com/github/newhouseb/handcrafted/blob/main/HandCrafted.ipynb
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