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Погружаемся в машинное обучение и Data Science

Показываем как запускать любые LLm на пальцах.

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An Ethical Application of Computer Vision and Deep Learning — Identifying Child Soldiers Through Automatic Age and Military Fatigue Detection

https://www.pyimagesearch.com/2020/05/11/an-ethical-application-of-computer-vision-and-deep-learning-identifying-child-soldiers-through-automatic-age-and-military-fatigue-detection/
Galaxy Zoo: Classifying Galaxies with Crowdsourcing and Active Learning

In this tutorial you will know how to use crowdsourcing and machine learning to investigate how galaxies evolve by classifying millions of galaxy images.

https://blog.tensorflow.org/2020/05/galaxy-zoo-classifying-galaxies-with-crowdsourcing-and-active-learning.html

Code: https://github.com/mwalmsley/galaxy-zoo-bayesian-cnn/blob/88604a63ef3c1bd27d30ca71e0efefca13bf72cd/zoobot/active_learning/acquisition_utils.py#L81
Graph Structure Learning for Robust Graph Neural Networks

A general framework Pro-GNN, which can jointly learn a structural graph and a robust graph neural network model from the perturbed graph guided by these properties.

Github: https://github.com/ChandlerBang/Pro-GNN

Paper: https://arxiv.org/abs/2005.10203
SymJAX: symbolic CPU/GPU/TPU programming

SymJAX is a symbolic programming version of JAX simplifying graph input/output/updates and providing additional functionalities for general machine learning and deep learning applications.

docs: https://symjax.readthedocs.io/en/latest/

github: https://github.com/RandallBalestriero/SymJAX

pdf: https://arxiv.org/pdf/2005.10635v1.pdf
Evaluating Natural Language Generation with BLEURT

BLEURT (Bilingual Evaluation Understudy with Representations from Transformers) builds upon recent advances in transfer learning to capture widespread linguistic phenomena, such as paraphrasing

https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.html

Github: https://github.com/google-research/bleurt

Paper: https://arxiv.org/abs/2004.04696
DETR: End-to-End Object Detection with Transformers

PyTorch training code and pretrained models for DETR The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture.

Github: https://github.com/facebookresearch/detr

Paper: https://arxiv.org/abs/2005.12872v1

Code: https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_demo.ipynb
How MTS used smart contract to build a system for selecting best technological projects.

https://habr.com/ru/company/ru_mts/blog/504058
Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity

The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available.

Github: https://github.com/lindawangg/COVID-Net

Paper: https://arxiv.org/abs/2005.12855v1
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Segmentation Loss Odyssey

Loss functions for image segmentation

Github: https://github.com/JunMa11/SegLoss

Paper: https://arxiv.org/abs/2005.13449v1