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nni

microsoft / nni

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.view() leading to error during ModelSpeedup

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shenoynikhil98
shenoynikhil98 commented Mar 23, 2022

https://github.com/microsoft/nni/blob/8d5f643c64580bb26a7b10a3c4c9accf617f65b1/nni/compression/pytorch/speedup/jit_translate.py#L382

While trying to speedup my single shot detector, the following error comes up. Any way to fix this,

/usr/local/lib/python3.8/dist-packages/nni/compression/pytorch/speedup/jit_translate.py in forward(self, *args)
    363 
    364         def forward(self, *
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good first issue support ModelSpeedup
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ZhaoJ9014 / face.evoLVe

Star 2.8k

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

machine-learning computer-vision deep-learning pytorch artificial-intelligence feature-extraction supervised-learning face-recognition face-detection tencent transfer-learning nus convolutional-neural-network data-augmentation face-alignment imbalanced-learning model-training fine-tuning face-landmark-detection hard-negative-mining
  • Updated Dec 9, 2021
  • Python

meyda / meyda

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Open

All parameternames in csv output

4
Oortone
Oortone commented Oct 26, 2021

Is your feature request related to a problem? Please describe.
Using the CLI to output parameters, those audio features who are multidimensional like amplitudeSpectrumand mfcc will not get corresponding label names in the first row of the generated csv-file. This makes it complicated to import using csv-importers like pandas in Python.
It's also unclear which bin each column represents.

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good first issue cli
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feature_engine

feature-engine / feature_engine

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time series forecasting: window features

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solegalli
solegalli commented Dec 17, 2021

The transformer should create computations over windows of past values of the features, and populate them at time t, t being the time of the forecast.

It uses pandas rolling, outputs several comptutations, mean, max, std, etc, and pandas shift to move the computations to the right row.

tmp = (data[variables]
       .rolling(window='3H').mean()  # Average the last 3 hr values.
       .
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new feature good first issue

LastAncientOne / Deep-Learning-Machine-Learning-Stock

Star 544

Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.

data-science machine-learning deep-learning trading algorithms prediction data-visualization feature-selection feature-extraction stock-market stock-price-prediction data-analysis stock-data feature-engineering stock-prices stock-prediction stock-analysis financial-engineering stock-trading features-extraction
  • Updated Mar 18, 2022
  • Jupyter Notebook

ashishpatel26 / Amazing-Feature-Engineering

Star 332

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

data-science machine-learning data-mining deep-learning scikit-learn data-visualization feature-selection feature-extraction data-analysis data-scientists feature-engineering features feature-scaling
  • Updated Nov 29, 2020
  • Jupyter Notebook

towhee-io / towhee

Star 322
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mesutpiskin / computer-vision-guide

Star 306

📖 This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

opencv machine-learning deep-neural-networks computer-vision deep-learning camera-calibration image-processing feature-extraction image-classification face-recognition object-detection optical-character-recognition background-subtraction stereo-vision image-preprocessing image-filtering goruntu-isleme
  • Updated Feb 10, 2022
  • Python

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