The Pleasure of Finding Things Out – The Pleasure of Finding Things Out: A blog by James Triveri

Streamlining Multi-App Deployment with Docker and NGINX

Python

R

Streamlining multi-app deployment with Docker and NGINX

Up and Running with JAX - Backpropagation and Training Neural Networks

Machine Learning

Python

Implementing the loss function, backward pass and training loop in JAX

Up and Running with JAX - Fully-Connected Network Forward Pass

Machine Learning

Python

Implementing the forward pass of a fully-connected neural network using JAX

Up and Running with JAX - JIT Compilation, Vectorizing Transformations and autodiff

Machine Learning

Python

Introduction to the JAX library for high-performance numerical computing in Python.

Creating Animations with Folium

Python

Geospatial

Creating animations with folium

The Bootstrap Chain Ladder from Scratch using Polars

Python

Actuarial

The bootstrap chain ladder from scratch using polars

Standing Up Ad-hoc Compute Clusters with Dask

Python

Creating ad-hoc compute clusters with dask

Multimodal Neural Networks for Risk Classification

Machine Learning

Python

Implementing multi-modal neural networks with pytorch

Geohashing for Fun and Profit

Python

Geospatial

Geohashing from scratch in python

GIS Algorithms in Python, Part I - Point-to-Point and Point-to-Line Distances

Geospatial

Python

Computing point-to-point and point-to-line distances in Python

The Bootstrap Chain Ladder from Scratch using Pandas

Python

Actuarial

The bootstrap chain ladder from scratch using Pandas

Quantifying Spatial Autocorrelation with Moran’s I

Python

Geospatial

Quantifying spatial autocorrelation with Moran’s I

Determining Distance to Coastline for Policy Locations Using GeoPandas

Python

Geospatial

Determining distance to coastline for policy locations using GeoPandas

A Data-Driven Approach to Ranking Teams in Uneven Paired Competition

Machine Learning

Python

A data-driven approach to ranking teams in uneven paired competition

Techniques for Convolutional Neural Network Interpretability

Machine Learning

Python

Techniques for convolutional neural network interpretability

Generating Correlated Boostrap Reserve Estimates

Statistical Modeling

Actuarial

R

Generating correlated bootstrap reserve estimates with R

Introduction to Markov Chain Monte Carlo - The Metropolis-Hastings Algorithm

Statistical Modeling

Python

Introduction to markov chain monte carlo - The Metropolis-Hastings algorithm

Creating a Blog with Quarto and GitHub Pages

Other

Creating a blog with quarto and github pages

Introduction to Folium

Python

Geospatial

Introduction to Folium

Working with Geospatial Vector Data

Python

Geospatial

Working with geospatial vector data

Composite Estimators in scikit-learn

Machine Learning

Python

Combining preprocessing and classifier within a single pipeline

Four Approaches to Creating Runoff Triangles in R

R

Statistical Modeling

Actuarial

Four approaches to creating runoff triangles in R

Querying SQL Server from Pandas

Python

Querying SQL Server from Pandas

Hyperparameter Search and Classifier Threshold Selection

Machine Learning

Python

Hyperparameter search and classifier threshold selection

Using the Singular Value Decomposition for Image Compression

Machine Learning

Python

Using the singular value decomposition for image compression

The Doomsday Argument

Other

Walking through the probabilities associated with the doomsday argument

Estimating Logistic Regression coefficients From Scratch in Python

Statistical Modeling

Python

Fitting logistic regression models with iterative reweighted least squares in Python

Assessing Model Goodness-of-Fit in Python with Scipy

Statistical Modeling

Python

Assessing model goodness-of-git in Python with Scipy

Estimating Logistic Regression coefficients From Scratch in R

Statistical Modeling

R

Fitting logistic regression models with iterative reweighted least squares in R

Backpropagation for Fully-Connected Neural Networks

Machine Learning

Python

Backpropagation for fully-connected neural networks

Creating scikit-learn Pipelines

Machine Learning

Python

Creating scikit-learn pipelines

Shared Data Parallel Processing in Python

Python

Sharing data between processes with Python’s multiprocessing library

Finding Roots of Equations in R with uniroot

Statistical Modeling

R

Finding roots of equations in R with uniroot

LogLikelihood Estimation in R with optim

Statistical Modeling

R

LogLikelihood estimation in R with optim

A PDF Harvester in 25 Lines of Python

Python

A PDF harvester in 25 Lines of python

Online Mean and Variance Update without Full Recalculation

Statistical Modeling

Python

Updating sample mean and variance to account for new observations without full recalculation

Performing Rolling Joins with data.table

R

Performing rolling joins with data.table

Smoothing Data with Cubic Splines in R

Statistical Modeling

R

Smoothing data with cubic splines in R

The Beta Function and its Variants

Statistical Modeling

An investigation of the complete and incomplete beta functions with use cases

Accessing C Library Functions from Python

Python

Accessing C library functions from Python

Using Dijkstra’s Algorithm to Find All Shortest Paths in a Graph

Statistical Modeling

Python

Using Dijkstra’s algorithm to find all shortest paths in a graph

Expectation Maximization from Scratch in R

Machine Learning

R

Expectation maximization from scratch in R

Understanding the Graph Convolutional Network Propogation Model

Machine Learning

Python

Walking through the forward pass described in the Kipf and Welling paper

Bayesian Linear Regression via Gibbs Sampling

Statistical Modeling

Python

Gibbs sampling approaches for Bayesian linear regression

Speeding Up R with Rcpp

R

Speeding Up R Code with Rcpp

Bessel’s Correction

Statistical Modeling

Python

Correcting the bias in estimators of population variance

User-Defined Binary Operators in R

R

Implementing custom binary operators in R

Generating Correlated Random Samples in Python

Statistical Modeling

Python

Generating correlated random samples in python

Aggregation with data.table

R

Aggregation with data.table

Introduction to Special Symbols in data.table

R

Introduction to special symbols in data.table

Derivation of the Normal Equations

Statistical Modeling

Derivation of the Normal Equations via least squares and maximum likelihood

Computing the Product of a Variable Number of Columns using data.table

R

Computing the product of a variable number of columns using data.table

Denoising Signals using the FFT

Machine Learning

Python

Denoising signals using the FFT

Utilizing Zero-Width Assertions with grep

Linux

Utilizing zero-width assertions with grep

Gradient Descent for Logistic Regression

Machine Learning

Python

Implementing gradient descent to estimate logistic regression coefficients

A Matrix Factorization Approach to Linear Regression

Statistical Modeling

Python

A matrix factorization approach to linear regression

Parameterized Decorators in Python

Python

Parameterized decorators in Python

Derivation of the Poisson Distribution as a Limiting Case of the Binomial PDF

Statistical Modeling

Derivation of the Poisson distribution as a limiting case of the binomial PDF

Simple Example of RSA Encryption and Decryption

Python

An illustrative example of RSA encryption and decryption using small primes

Querying SQL Server with Pandas

Python

Querying SQL server with Pandas

Working with SQLite in R

R

Working with SQLite in R

The Buried Assumption in Constant Dispersion Tweedie Models

Statistical Modeling

Actuarial

The buried assumption in constant dispersion tweedie models

Creating R Packages with devtools and roxygen

R

Creating R packages with devtools and roxygen

The Elbow Method for Determining the Number of Clusters in K-Means

Machine Learning

Python

The elbow method for determining the number of clusters in k-keans

Demonstration of Principal Component Analysis

Machine Learning

Python

Demonstration of principal component analysis