I’m Alexis Huet. I got a PhD of mathematics in 2014 from the University of Lyon (France). I’m working in data science.

On this blog, I provide detailed posts about deep learning, maths in data science, and describe some own projects. I code in R and Python , sometimes in Javascript and C++ .

Contact me

alexis.huet.phd@gmail.com

Q/A on Stackoverflow

• Keras RNN with LSTM cells for predicting multiple output time series based on multiple intput time series (answer)
• Good accuracy despite high loss value (answer)
• Clustering as dimensionality reduction (question)
• Remove anti-aliasing for pandas plot.area (question)

Posts about deep learning

• RNN with Keras: Predicting time series Complete introduction of time series prediction with RNN. This tutorial has been written for answering a stackoverflow post, and has been used later in a real-world context.

• RNN with Keras: Understanding computations Highlights structure of common RNN algorithms by following computations carried out by each model. It provides a clear summary of command lines, math equations and diagrams.

Posts about maths in data science

• Optimizing GMM parameters using EM. Description of GMM; How to update parameters using EM; Illustration on a simple example. Unlike many other sources, I fully detail parameters’ update using gradient and Hessian.

• Rediscover EM algorithm from scratch. Many introductions of EM exist on the web. This one starts from the likelihood computation problem and uses inductive reasoning to bring out EM.

• Computation of the gradient for SNE. Deriving gradient of the SNE algorithm, fully detailed.

• An illustration of Metropolis–Hastings algorithm. Toy example for understanding Metropolis–Hastings algorithm on a simple example.

• Maximizing likelihood is equivalent to minimizing KL-divergence. Restating this classic equivalence in my “own” words.

• Introduction to particle filters Introduction to particle filters, with an homemade example of trajectory tracking.

• Introduction to hidden Markov models Introduction to hidden Markov models on finite state spaces, following the tutorial of L. R. Rabiner.

Own projects

• Langton’s ant extended to Voronoi tessellations A program extending Langton’s ant to any Voronoi tessellation of the plane. Simulations show interesting walks for some partitions of the plane, including chaotic structures, highway patterns and even bounded evolutions.

• Anabasis webapp Webapp where players can draw collaborative paintings. It was built with node.js combined with mongodb, and hosted on Heroku and MLab. Analysis of collected data is also available in this post.

• Trigger snake A challenging snake game built in C++/Qt4

• Nim function for take-a-prime game Simulation of a recursive math sequence with interesting patterns, accelerated using C++ language.

• Coal: Composition of Linear Functions A program for automating composition of linear functions. gmp package has been used to keep exact results for big rational numbers.

• Triangle pursuit A program computing recurrent sequences, offering generalization for different rules, different norms, larger number of initial points and higher dimensions.

• Gender of French nouns Check out the distribution of the gender of French nouns across the letters.

• Description and modeling of FlapMMO score data FlapMMO is an online game similar to Flappy Bird. This post explores a collected dataset of scores, using descriptive statistics and testing probabilistic models.

• Watering and draining planets And what would be the Moon, Mars and Venus with as much water in proportion as on the Earth?

• An enigma Could you find the missing symbol?