Explores performance optimizations for scikit-learn's GridSearchCV by using closed-form solutions and warm starts for specific linear models.
Introducing Linear Diffusion, a novel diffusion model built entirely from linear components for generating simple images like MNIST digits.
A tutorial on visualizing custom statistical models, including linear and non-linear fits, using ggplot2 in R for data science workflows.
Explains the Neural Tangent Kernel concept through simple 1D regression examples to illustrate how neural networks evolve during training.
Debunks the myth that models can't have more parameters than data points, explaining how and when under-determined models can be solved and useful.
Explains how to parallelize QR decomposition for linear models on big data using R's biglm package and incremental merging.
