Last Friday was PyData Paris, in words of the organizers, ''a gathering of users and developers of data analysis tools in Python''.

The organizers did a great job in putting together and the event started already with a full room for Gael's keynote

My take-away message from the talks is …

My latest research paper[^{1}] deals with the estimation of the hemodynamic response function (HRF) from fMRI data.

This is an important topic since the knowledge of a hemodynamic response function is what makes it possible to extract the brain activation maps that are used in most of the impressive …

One of the lesser known features of the memory_profiler package is its ability to plot memory consumption as a function of time. This was implemented by my friend Philippe Gervais, previously a colleague at INRIA and now at Google.

With this feature it is possible to generate very easily a …

TL; DR These are some notes on calibration of surrogate loss functions in the context of machine learning. But mostly it is an excuse to post some images I made.

In the binary-class classification setting we are given $n$ training samples $\{(X_1, Y_1), \ldots, (X_n, Y_n)\}$, where $X_i$ belongs to …

As part of the development of
memory_profiler I've tried
several ways to get memory usage of a program from within Python. In this post
I'll describe the different alternatives I've tested.

### The psutil library

psutil is a python library that provides
an interface for retrieving information on running processes. It …

In this post I compar several implementations of
Logistic Regression. The task was to implement a Logistic Regression model
using standard optimization tools from `scipy.optimize`

and compare
them against state of the art implementations such as
LIBLINEAR.

In this blog post I'll write down all the implementation details of …

**TL;DR: I've implemented a logistic ordinal regression or
proportional odds model. Here is the Python code**

The *logistic ordinal regression* model, also known as the
proportional odds was introduced in the early 80s by McCullagh [^{1}, ^{2}]
and is a generalized linear model specially tailored for the case of …

My latest contribution for scikit-learn is
an implementation of the isotonic regression model that I coded with
Nelle Varoquaux and
Alexandre Gramfort. This model
finds the best least squares fit to a set of points, given the
constraint that the fit must be a non-decreasing
function. The example
on the …

Householder matrices are square matrices of the form

$$ P = I - \beta v v^T$$

where $\beta$ is a scalar and $v$ is a vector. It has the useful
property that for suitable chosen $v$ and $\beta$ it makes the product
$P x$ to zero out all of the coordinates but …

** Note: this post contains a fair amount of LaTeX, if you don't
visualize the math correctly come to its original location **

In machine learning it is common to formulate the classification task
as a minimization problem over a given loss function. Given data input
data $(x_1, ..., x_n)$ and associated labels …