Projects

by Séb Arnold, May 22, 2018

\( % Universal Mathematics \newcommand{\paren}[1]{\left( #1 \right)} \newcommand{\brackets}[1]{\left[ #1 \right]} \newcommand{\braces}[1]{\left\{ #1 \right\}} \newcommand{\norm}[1]{\left\lVert#1\right\rVert} \newcommand{\case}[1]{\begin{cases} #1 \end{cases}} \newcommand{\bigO}[1]{\mathcal{O}\left(#1\right)} % Analysis % Linear Algebra \newcommand{\mat}[1]{\begin{pmatrix}#1\end{pmatrix}} \newcommand{\bmat}[1]{\begin{bmatrix}#1\end{bmatrix}} % Probability Theory \DeclareMathOperator*{\V}{\mathop{\mathrm{Var}}} \DeclareMathOperator*{\E}{\mathop{\mathbb{E}}} \newcommand{\Exp}[2][]{\E_{#1}\brackets{#2}} \newcommand{\Var}[2][]{\V_{#1}\brackets{#2}} \newcommand{\Cov}[2][]{\mathop{\mathrm{Cov}}_{#1}\brackets{#2}} % Optimization \newcommand{\minimize}{\operatorname*{minimize}} \newcommand{\maximize}{\operatorname*{maximize}} \DeclareMathOperator*{\argmin}{arg\,min} \DeclareMathOperator*{\argmax}{arg\,max} % Set Theory \newcommand{\C}{\mathbb{C}} \newcommand{\N}{\mathbb{N}} \newcommand{\Q}{\mathbb{Q}} \newcommand{\R}{\mathbb{R}} \newcommand{\Z}{\mathbb{Z}} \)

Randopt

Randopt

Randopt is a Python package for machine learning experiment management, hyper-parameter optimization, and results visualization. It is in active development and I -- as well as others -- have been using it for every machine learning project since November 2016. You can install it with pip install randopt.

[GitHub Repo, randopt.ml]


Tooski

Tooski.ch

Tooski is the largest francophone website dedicated to the Ski World Cup. There, you'll find news and blogs related to the Swiss Ski Team, the FIS World Cup circuit, as well as some younger skiers. Tooski began as an adventure in merging two of my passions: computers and skiing.

[tooski.ch]


Kleo the Cat

Kleo the cat

In the Summer of 2017, Theo Denisart (mostly) and I spent some time designing and programming a 3D-printed robotic cat for reinforcement learning, while at the ValeroLab. What makes Kleo special is that its limbs are actuated via tendons -- making it robust to failures but difficult to control. Matt Simon brilliantly covered our work in his WIRED article.

[WIRED Article]