TGINSIGHT SIMILAR POSTS
Source channel @githubtrending · Post #15384 · Jan 2
#other#awesome#chartjs#charts#integrations#plugins#resources Chart.js is a flexible JavaScript library for creating interactive charts with extensive customization options. You can use it with popular frameworks like React, Vue, and Angular through dedicated adapters, and extend its functionality with plugins for styling, features, and data handling. The library supports three major versions—v2 (April 2016), v3 (April 2021), and v4 (November 2022)—each with different plugin compatibility. This means you can choose the version that best fits your project needs and find compatible extensions for charts, animations, zooming, data labels, and more. Whether you need basic charts or advanced visualizations with custom interactions, Chart.js provides the tools to build professional data displays efficiently. https://github.com/chartjs/awesome
Search: #dl
@repo_science · Post #4131 · 05/18/2024, 09:06 PM
#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 · 11/21/2025, 07:53 AM
#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 · 10/05/2025, 07:41 AM
#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 · 06/28/2025, 07:04 AM
#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 · 06/14/2025, 08:43 AM
#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 · 02/21/2025, 07:42 AM
#dl Sure, nobody uses it anyways.
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@amneumarkt · Post #625 · 10/03/2024, 09:31 PM
#dl PyTorch Native Architecture Optimization: torchao | PyTorch https://pytorch.org/blog/pytorch-native-architecture-optimization/
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@amneumarkt · Post #624 · 10/03/2024, 06:07 PM
#dl https://soumith.ch/blog/2024-10-02-training-10k-scale.md.html
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@amneumarkt · Post #602 · 07/20/2024, 05:48 AM
#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 · 03/16/2024, 09:09 AM
#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 · 11/13/2023, 08:30 AM
#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 · 06/12/2023, 06:13 AM
#dl https://twitter.com/armin_kekic/status/1667181047480479751?s=20
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