Nicolas Chagnet's Homepage

Useful example of a git worktree workflow.

A description of the effect of binary quantization on embeddings.

An interesting CLI tool designed to call on to LLMs from the CLI with isolated short prompts.

Some interesting reflections on how to use LLMs in daily development work.

An interesting series of articles explaining how machine learning models memorize data.

I'm not a great SQL user, I have experience (mainly from database management in web development and now as a data scientist) but I don't consider myself an SQL wizard.

An in-depth example of how to fine-tune an LLM using the Hugging Face ecosystem.

A useful Python library to sample custom probability distributions.

A Python library for timeseries forecasting with very extensive features.

An illustrated introduction to Bayesian statistics using Jupyter notebooks.

A rust-based alternative to neovim with opinionated defaults.

Explanation of LoRA methods for LLMs.

An interesting reflection on markup languages for recipes.

A book introducing Rust for programmers with experience in other languages.

Fantastic introduction to the simulated annealing metaheuristic in Python.

A short blog post summarizing the main compression algorithms.

Example of what a linklog should look like, and what I am for with my own.

Toy example of how to use neural networks to solve differential equations.

Interesting dive on how to handle multiple markup languages in a Rust-based static website generator.

An interesting dive into what makes diffusion models work.

In-depth explanation of LSTM Networks.

Introduction and example of how to build a recurrent neural network from scratch.

Musings about the use of data contracts to validate data sources.

Article about the use of Polars for both production and development stages.

Great online book about Polars targeted to Pandas users.

A guide on how to use "einsum" in Python for tensor manipulation.

This is how I built an end-to-end timeseries prediction pipeline.

Advice on agentic workflow for practical applications from Anthropic.

A useful guide for hyperparameter tuning of LGBM models.

My blogging and project goals for 2025.

A series of examples of proper software design in data science beyond Jupyter notebooks.

A short list of best practices.

Detailed use cases of the OR-Tools library for optimization problems.

I always have to google Github actions format and snippets, or prompt an LLM for it.

Online book on how to use command-line tools for quick data science results.

Some interesting thoughts on diffusion models.

An in-depth description of the connections between diffusion models and Riemannian geometry.

A handy rust-based TUI application to view and manipulate data from CSV and databases.

This a collection of all the optimization metaheuristic you can possibly imagine, with comments on how to implement them and what parameters can be tuned.

A collection of resources to help make better data visualizations.

Thorough explanation of the Transformers model.

Bring some sanity to Python and remove null checks.

Series of articles on best practices around Python coding and tooling.

Useful library to estimate feature importance of machine learning models, based on game theory principles.

A set of best-practices in Python development.

An online book focusing on the fundamentals of data science (statistics, traditional machine learning).

A beautiful set of visualizations of common algorithms.

I always love when data scientists take it too far on their hobbies.

How to simulate the physics of magnets using Monte-Carlo methods and genetic algorithms.

Natural language processing toolkit optimized for CPU hardware.