Tutorials
The following are links to PDF documents available on researchgate.
numpy/scipy recipes for data science and machine learning
- subset-constrained vector quatization via mean discrepancy minimization
- mean discrepancy minimization for vector quantization
- information theoretic vector quantization
- support vectors of minimum enclosing balls
- Frank-Wolfe for minimum enclosing balls
- projections onto the standard simplex
- archetypal analysis via Frank-Wolfe optimization
- archetypal analysis via mirror descent
- archetypal analysis for clustering
- archetypal analysis for classification
- linear programming
- training neural networks without backpropagation
- spectral clustering
- computing nearest neighbors
- eigenvalues/eigenvectors of covariance matrices
- computing the Kullback-Leibler divergence between generalized gamma distributions
numpy/scipy recipes for image processing and analysis
- drawing the dragon (1)
- drawing the dragon (2)
- creating fractal images
- simple intensity transformations
- intensity normalization and histogram equalization
- binary images and morphological operations
- avoiding for loops over pixel coordinates
- affine image warping
- conditional affine image warping
- general image warping