Description

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