Machine learning books and papers
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When the code works anyway



♥️😂 @Machine_learn
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Abstract: SuperpixelGridMasks. It is a data augmentation approach which permits to generate various complementary images from original sensor-based data of varied natures e.g. X-Ray scans, vehicular scenes, people images (see data samples). This approach allows to increase the size of your image-based training datasets towards expecting better performances in your analysis tasks. Experiments have shown that the approach can be efficient for image classification tasks.

Link: https://www.researchgate.net/publication/360062941_SuperpixelGridCut_SuperpixelGridMean_and_SuperpixelGridMix_Data_Augmentation
@Machine_learn
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FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Github: https://github.com/hpcaitech/fastfold

Paper: https://arxiv.org/abs/2203.00854v1

@Machine_learn
1-s2.0-S154461232100218X-main.pdf
1.8 MB
Cryptocurrency liquidity and volatility interrelationships during
the COVID-19 pandemic #Paper @Machine_learn
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2109.12142.pdf
4.1 MB
Periodicity in Cryptocurrency Volatility and Liquidity #Paper @Machine_learn
1512.03385.pdf
800.2 KB
Deep residual learning for image recognition #paper
@Machine_learn
Predicting_academic_performance.pdf
900.3 KB
Predicting academic performance by considering student heterogeneity #Paper @Machine_learn
chapter 4.pdf
2.1 MB
Multi-Instance Multi-Stage
Deep Learning for Medical
Image Recognition #Chapter4 @Machine_learn
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