wehub-resource-sync
64 lines
2.6 KiB
Markdown
64 lines
2.6 KiB
Markdown
# Datasets
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This is a list of datasets that are used for Jupyter Notebook demos in this repository.
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### MNIST (Handwritten Digits)
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> [mnist-demo.csv](mnist-demo.csv)
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_Source: [Kaggle](https://www.kaggle.com/oddrationale/mnist-in-csv/home)_
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A sample of original MNIST dataset in a CSV format. Instead of using full dataset with 60000 training examples the dataset consists of just 10000 examples.
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Each row in the dataset consists of 785 values: the first value is the label (a number from 0 to 9) and the remaining 784 values (28x28 pixels image) are the pixel values (a number from 0 to 255).
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### Fashion MNIST
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> [fashion-mnist-demo.csv](fashion-mnist-demo.csv)
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_Source: [Kaggle](https://www.kaggle.com/zalando-research/fashionmnist)_
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Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
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Instead of using full dataset with 60000 training examples we will use cut dataset of just 5000 examples that we will also split into training and testing sets.
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### World Happiness Report 2017
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> [world-happiness-report-2017.csv](world-happiness-report-2017.csv)
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_Source: [Kaggle](https://www.kaggle.com/unsdsn/world-happiness#2017.csv)_
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Happiness rank and scores by country, 2017.
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### Iris Flowers
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> [iris.csv](iris.csv)
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_Source: [ics.uci.edu](http://archive.ics.uci.edu/ml/datasets/Iris)_
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Iris data set data set consists of several samples from each of three species of Iris (`Iris setosa`, `Iris virginica` and `Iris versicolor`). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.
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### Microchips Tests (Artificial)
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> [microchips-tests.csv](microchips-tests.csv)
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_Source: [Machine Learning at Coursera](https://www.coursera.org/learn/machine-learning)_
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Artificial dataset in which `param_1` and `param_2` produce non-linear decision boundary.
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### Non-Linear Y(X) Dependency (Artificial)
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> [non-linear-regression-x-y.csv](non-linear-regression-x-y.csv)
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_Source: [Machine Learning at Coursera](https://www.coursera.org/learn/machine-learning)_
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Artificial dataset that contains non-linear y(x) dependency.
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### Server Operational Parameters
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> [server-operational-params.csv](server-operational-params.csv)
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_Source: [Machine Learning at Coursera](https://www.coursera.org/learn/machine-learning)_
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Dataset of server operational parameters containing the `Latency(Throughput)` dependency.
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