TGINSIGHT SIMILAR POSTS
Source channel @olddriverGDstudy · Post #51 · Mar 24
#上头诫#知识 噫吁嚱，呜呼哀哉。佳丽之心， 如渊似海云雾间。前有鬼者心有属，今有上将四人间。心似骄阳深似火，怎当白桓是真心。柳间戏水不得喻，错将弱颜当磐石。今日不见凄鬼之心散步言语现，此时却如千万金石尽如吼头甜。千言万语悬浮脑海间，百转千回纠缠心火炼。上将游戏四水间，怎奈四水通流涧。不得可可不得乖，碧水深潭心坏怜。心知真己不觉少，奈何四水风见消。索向索梁不觉走，回神已在深涧见。深涧云气鬼雾袅，崖山悬顶有佳囡。云烟做红霞，鬼雾做红妆。似是云波似是锦，可文鬼泣是有心。东升日出朝阳起，云散无效鬼泪去。不知南柯曾觉晓，梦里梦外梦惺惺。囡囡心念念，鬼鬼向戚戚。柳七窃窃似潇潇，新年却已入人牢。谁知何时却明晓，涉水不足总深腰。无问无知无所念，有情有景有春宵。尽知尽晓秀哥谣，不管不顾十诫飘。愿此流真做悲景，莫要上头惹人笑。
Search: #ml
@repo_science · Post #3688 · 11/03/2023, 12:00 PM
#ML 😎 FREE RESOURCES TO LEARN MACHINE LEARNING Intro to ML by MIT Free Course Machine Learning for Everyone FREE BOOK ML Crash Course by Google Advanced Machine Learning with Python Github Practical Machine Learning Tools and Techniques Free Book Python Machine Learning for beginners ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----
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@repo_science · Post #3447 · 07/17/2023, 03:14 PM
#ML 🧠 Machine Learning Expert El aprendizaje automático es un vasto campo con muchos conceptos clave que conocer. Nuestro curso intensivo cubre todos los componentes básicos que necesita para sumergirse en el aprendizaje automático del mundo real. ✍️Ryan Doan | Ex-Amazon ML Infrastructure Engineer 🌐En 📆2022 🔗Link ----- Main channel:@repo_science Coupons:@freecoupons_reposcience -----
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@amneumarkt · Post #687 · 07/18/2025, 06:24 AM
#ml https://aryagxr.com/blogs/intro-to-nmc
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@amneumarkt · Post #682 · 06/27/2025, 05:30 AM
#ml Machine Learning Visualized — Machine Learning Visualized https://ml-visualized.com/?utm_source=substack&utm_medium=email
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@amneumarkt · Post #617 · 08/25/2024, 02:03 PM
#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 · 07/30/2024, 06:20 AM
#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, 08:53 PM
#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 · 07/06/2024, 10:02 PM
#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 · 05/09/2024, 07:51 PM
#ml https://github.com/google-research/timesfm
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@amneumarkt · Post #538 · 02/16/2024, 11:21 AM
#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 · 02/09/2024, 05:35 AM
#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 · 02/05/2024, 10:57 AM
#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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