Episode 28: Everything’s Coming Up Machine Learning

In this episode, I netted new ways to knock neural networks out of the park, let R do my calculus calculations for me, garnered gentle overviews to many   machine learning topics, and made sure my phylogenic trees were publication ready with the R package ‘CancerEvolutionVisualization'. References: Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia calculus: High-Dimensional Numerical and Symbolic Calculus in R Deep Image Prior for medical image denoising, a study about parameter initialization On Physics-Informed Neural Networks for Quantum Computers Not frequentist enough. ggradar: radar plots with ggplot in R Mastering Debugging in R Understanding leaf node numbers when using rpart and rpart.rules A Gentle Introduction to using Support Vector Machines for Classification Boosting in Machine Learning: A Brief Overview Algorithm Classifications in Machine Learning R-packages: pirouette: Create a Bayesian Posterior from a Phylogeny CancerEvolutionVisualization: Publication Quality Phylogenetic Tree Plots odetector: Outlier Detection Using Partitioning Clustering Algorithms stats4teaching: Simulate Pedagogical Statistical Data camcorder: Record Your Plot History openxlsx2: Read, Write and Edit 'xlsx' Files