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yzhao062 / pyod

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pycaret / pycaret

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to_csv is slow and cost lot storage. Should use to_feather(), use arrow ipc format?

SheldonXLD
SheldonXLD commented Dec 30, 2021

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is.

to_csv is slow and cost lot storage

Describe the solution you'd like
A clear and concise description of what you want to happen, ideally taking into consideration the existing toolbox design, classes and methods.

use to_feather to make it more efficient and sm

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enhancement good first issue
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RubixML / ML

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Star 1.5k

A high-level machine learning and deep learning library for the PHP language.

php data-science machine-learning natural-language-processing algorithm ai deep-learning neural-network analytics clustering cross-validation regression prediction artificial-intelligence classification rubix machine-learning-library feature-vector hidden-layers anomaly-detection classification-trees regression-trees php-ml php-ai php-machine-learning
  • Updated Jan 5, 2022
  • PHP

curiousily / Getting-Things-Done-with-Pytorch

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Star 1.4k

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

nlp machine-learning tutorial computer-vision deep-learning time-series sentiment-analysis pytorch transformer lstm yolo face-recognition face-detection object-detection transfer-learning bert anomaly-detection time-series-forecasting coronavirus time-series-anomaly-detection
  • Updated Jun 14, 2021
  • Jupyter Notebook
flow-forecast

AIStream-Peelout / flow-forecast

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shubhomoydas / ad_examples

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A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated Aug 25, 2021
  • Python

curiousily / Deep-Learning-For-Hackers

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Star 728

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

python machine-learning deep-learning sentiment-analysis tensorflow keras artificial-intelligence neural-networks object-detection jupyter-notebooks autoencoders tensorflow-tutorial bert image-augmentation anomaly-detection time-series-classification time-series-forecasting lstms intent-recognition
  • Updated Apr 23, 2020
  • Jupyter Notebook

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