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datacamp-assignment-pandas

Assignement for Datacamp on pandas.

datacamp-assignment1

datacamp-master

Datacamp class for master student - 1 week

explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

human_locomotion

RAMP starting kit on the human locomotion data set

indices-de-mutualisation

lectures-labs

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris

predictive-analysis-of-mobile-app-success

Nowadays, mobile applications are part of our daily lives. They now account for more than half (52%) of the time spent on digital media on the one hand, and would account for 89% of the time spent by users of on their smartphone.Thereby, it is not surprising that since 2016, the market for mobile applications has experienced unprecedented growth. It is now worth billions of dollars, through the marketing of mobile games in particular, and it explains the attractivity of this market for developers and investors.

sequential_proposal_mcmc

In this notebook we will analyze the work of Joonha Park and Yves Atchadé in the article Markov chain Monte Carlo algorithms with sequential proposals., published online on the 24th June 2020 (part of Springer Nature 2020). Markov Chain Monte Carlo methods are usually used to sample from a probability distribution. In the following, we consider the objective of obtaining samples from a target distribution π on a measurable space (X,X) , we assume that π has a density with respect to a reference measure denoted by λ (we also denote by π this density).

static-hashing-open

static-hashing-open-spark

test