To explore mathematical concepts behind machine learning algorithms and understand their inner working. Code is intentionally sub-optimal.
The Discrete Cosine Transform: Understanding the Discrete Cosine Transform (DCT)
Expectation–Maximization in Gaussian Mixture models: Expectation–Maximization in 1D Gaussian Mixture model using numpy
k-means clustering: Explore the math behind k-means, an unsupervised clustering algorithm, and understand its limitations.
Univariate Linear Regression: Univariate Linear Regression in numpy
Affine Transformations: Visualizing affine transformations in numpy
Convolution Kernels: Convolution matrices in image filtering applications
Eigen Decomposition: Eigen Values and Vectors
Principal Component Analysis: Principal Component analysis with numpy
Linear Discriminant Analysis: Linear Discriminant Analysis with numpy
Fourier Analysis: Fourier analysis with numpy and scipy
Norm: Euclidean, Taxicab and other vector norms
Projection Profile: Correcting document skew for imaging and machine learning applications
Prime Spirals: Visualizing prime numbers on a polar plot (Ulam spirals). 3blue1brown's video at https://www.youtube.com/watch?v=EK32jo7i5LQ&feature=share
Weierstrass Function: A pathological real valued function that is continuous everywhere, yet differentiable nowhere
Interpolation: Interpolation using scipy.interpolate
LaTeX: LaTeX markdown quick reference for use in these notebooks