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RubixML / RubixML

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Dynamic column width of Dataset string representation

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andrewdalpino
andrewdalpino commented Jun 19, 2020

String representations of dataset objects are used for previewing their contents from the terminal. When converting a Dataset object to a string, we build a table using ascii characters. The current table has fixed width columns that do not take full advantage of the terminal real estate if the dataset only contains a few columns.

echo $dataset;

<img width="574" alt="Annotation

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enhancement good first issue help wanted

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 Sep 25, 2020
  • Python

curiousily / Getting-Things-Done-with-Pytorch

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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 27, 2020
  • Jupyter Notebook

curiousily / Deep-Learning-For-Hackers

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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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