Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Delves into various color spaces, Color Deconvolution, and analyzing Whole Slide Images.
Covers the energy and radiation of LEDs, transitions in energy levels, and color synthesis.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
