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ZhaoJ9014 / face.evoLVe

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🔥🔥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 May 30, 2022
  • 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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feature_engine

feature-engine / feature_engine

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[NEW TRANSFORMER] exponential width discretiser

8
solegalli
solegalli commented May 7, 2022

when a variable is in a logarithmic scale, it might make sense to create the intervals based on a log scale instead of linear scale.

Quote:
"
When the numbers span multiple magnitudes, it may be better to group by powers of
10 (or powers of any constant): 0–9, 10–99, 100–999, 1000–9999, etc. The bin widths
grow exponentially
"

the idea is taken from: Feature Engineering for Machine Lear

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towhee-io / towhee

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LastAncientOne / Deep-Learning-Machine-Learning-Stock

Star 618

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 neural-network trading algorithms prediction feature-selection feature-extraction stock-market stock-price-prediction technical-analysis stock-data feature-engineering stock-prices stock-prediction stock-analysis financial-engineering stock-trading features-extraction
  • Updated Jul 17, 2022
  • Jupyter Notebook

ashishpatel26 / Amazing-Feature-Engineering

Star 360

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

mesutpiskin / computer-vision-guide

Star 316

📖 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 May 26, 2022
  • Python

x4nth055 / emotion-recognition-using-speech

Star 304

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

machine-learning deep-learning sklearn keras recurrent-neural-networks feature-extraction neural-networks support-vector-machine mfcc librosa emotion-detection gradient-boosting emotion-recognition kneighborsclassifier random-forest-classifier mlp-classifier speech-emotion-recognition emotion-recognizer
  • Updated May 26, 2022
  • Python

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