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Погружаемся в машинное обучение и Data Science

Показываем как запускать любые LLm на пальцах.

По всем вопросам - @haarrp

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UniverseNet

UniverseNet is the state-of-the-art detector that can be trained in 24 epochs.

Github: https://github.com/shinya7y/UniverseNet

Paper: https://arxiv.org/abs/2103.14027v1

@ai_machinelearning_big_data
👁 Learning Spatio-Temporal Transformer for Visual Tracking

Github: https://github.com/researchmm/Stark

Paper: https://arxiv.org/abs/2103.17154v1

@ai_machinelearning_big_data
📗 New updates: Interpretable Machine Learning

A Guide for Making Black Box Models Explainable.

https://christophm.github.io/interpretable-ml-book/

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Implicit Neural Representations with Periodic Activation Functions

Github: https://github.com/lucidrains/siren-pytorch

Paper: https://arxiv.org/abs/2006.09661

@ai_machinelearning_big_data
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PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks

Github: https://github.com/PaddlePaddle/PaddleGAN

Paper: https://arxiv.org/abs/2104.05376v1

@ai_machinelearning_big_data
🧠 Lite-HRNet: A Lightweight High-Resolution Network

Github: https://github.com/HRNet/Lite-HRNet

Paper: https://arxiv.org/abs/2104.06403

@ai_machinelearning_big_data
Microsoft's ProphetNet-X: Large-Scale Pre-training Models for English, Multi-lingual, Dialog, and Code Generation

Code: https://github.com/microsoft/ProphetNet

Paper: https://arxiv.org/pdf/2001.04063.pdf

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