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Focusing
Doctoral researcher in AI/ML for medical imaging at the University of Luxembourg.
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University of Luxembourg
- Luxembourg
- https://lu.linkedin.com/in/ben-bausch-a11270180
Highlights
- Pro
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Gist for linear classification
Gist for linear classification 1# disclaimer: example code not recommended for usage
23def covariance_estimation(X):
4mean_features = np.sum(X, axis=0) / X.shape[0]
5X = X - mean_features
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Gist for Kmeans
Gist for Kmeans 1import numpy as np
23def k_means(data, n_clusters):
4# choose n_clusters data points randomly for the initialization
5centroids_ids = np.random.choice(len(data), 3, replace=False)
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