Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Explores visualizing the Fourth Dimension through points, lines, circles, spheres, and punching through, covering vector space properties, dimensionality, bases, and theorems.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
