TGINSIGHT SIMILAR POSTS
Источник @procode404 · Post #3441 · 7 февр.
👨💻Программирование на Golang для начинающих Плейлист по разработке на go. В ходе уроков вы сможете познакомиться с основами языка и его синтаксисом, а также сможете на практике написать своего Telegram-бота. 1. Типы и переменные [15:01] 2. Переменные и ошибки с ними [16:26] 3. Циклы [14:41] 4. Массивы и срезы [9:33] 5. Словари [7:45] 6. Функции [10:14] 7. Структуры [10:28] 8. Парсер на Golang. Telegram и Go [13:54] 9. Telegram bot. Используем webhook [10:54] 10. Telegram bot. Собираем данные пользователей [6:13] 11. Фильтр сообщений для Telegram [5:11] 12. Telegram bot. Отправляем фото из облачного хранилища [7:12] 13. Упаковываем приложение в Docker [7:22] #go
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Поиск: #dl
@repo_science · Post #4131 · 18.05.2024, 21:06
#DL 📱 Zeus New Pytorch Ecosystem Tool Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads. 🖥Github ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----
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@amneumarkt · Post #706 · 21.11.2025, 07:53
#dl Introducing more symmetries in attention https://github.com/NVIDIA/torch-harmonics https://neurips.cc/virtual/2025/loc/san-diego/poster/117783
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@amneumarkt · Post #691 · 05.10.2025, 07:41
#dl Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12. https://www.nature.com/articles/s41467-025-63790-8
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@amneumarkt · Post #683 · 28.06.2025, 07:04
#dl A few cool ideas in this model. Introducing Gemma 3n: The developer guide - Google Developers Blog https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
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@amneumarkt · Post #681 · 14.06.2025, 08:43
#dl So tensorflow and jax are deprecated in the transformers package. https://github.com/huggingface/transformers/pull/38758
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@amneumarkt · Post #662 · 21.02.2025, 07:42
#dl Sure, nobody uses it anyways.
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@amneumarkt · Post #625 · 03.10.2024, 21:31
#dl PyTorch Native Architecture Optimization: torchao | PyTorch https://pytorch.org/blog/pytorch-native-architecture-optimization/
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@amneumarkt · Post #624 · 03.10.2024, 18:07
#dl https://soumith.ch/blog/2024-10-02-training-10k-scale.md.html
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@amneumarkt · Post #602 · 20.07.2024, 05:48
#dl There is this new lib called scale. One could compile CUDA code to use it on AMD GPU. https://docs.scale-lang.com/manual/how-to-use/ I don't know who is more pissed off, NVidia or AMD.
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@amneumarkt · Post #556 · 16.03.2024, 09:09
#dl This repo is really nice. yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models https://github.com/yuanchenyang/smalldiffusion
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@amneumarkt · Post #506 · 13.11.2023, 08:30
#dl Google & USC benchmarked a prompt based forecasting method, and the results are amazing. Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948
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@amneumarkt · Post #484 · 12.06.2023, 06:13
#dl https://twitter.com/armin_kekic/status/1667181047480479751?s=20
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