Description

In this episode, I marched through machine learning abstractions, brought home the BQN, discovered statistics projects to work on over Christmas, and found the Gosling that lays the golden egg,

References:

• Quantifying uncertainty of machine learning methods for loss given default
• Evaluating the performance of memory type logarithmic estimators using simple random sampling
• A fast kernel independence test for cluster-correlated data
• Autoencoders for sample size estimation for fully connected neural network classifiers
• [R-pkgs] onetime 0.1.0: Run Code Only Once
• 🚧 WIP 🚧 From Julia to BQN
• Using SAS to score a test
• Using the cspade action to find frequent gene sequences
• The easygoing relationship between computer scientists and null hypothesis significance testing
• Top 11+ Coding Projects for Beginners (2023 Edition)
• Gosling: Interactive Genomics Charts in R Shiny
• Gosling Main Page

R-packages:

• POMS:  Phylogenetic Organization of Metagenomic Signals
• macroBiome: A Tool for Mapping the Distribution of the Biomes and Bioclimate
• reservr: Fit Distributions and Neural Networks to Censored and Truncated Data
• NPCox: Nonparametric and Semiparametric Proportional Hazards Model