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

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