The state of the art methods in predicting consumer choice are Bayesian matrix factorization methods. This projects explores challenges and opportunities with using neural networks with embedding layers to predict restaurant choice from a large dataset of individual lunch choices in the Bay Area. Submitted as the final project for Stanford's Deep Learning class (CS230).
Synthesis of long peptides via solid phase peptide synthesis can often lead to a number of side products. This simple R Shiny web application is meant to help determine which problematic side products might be appearing in the LC-MS of the crude cleaved peptide mixture. The utility implements a simple recursive solution of the subset sum problem. Originally written for members of the Peter Kim lab at Stanford.
|grf||R||Boosted regression forests|