Name: Phil Paradis
Type: User
Company: MILA lab, Université de Montréal
Bio: I'm a mathematician with a deep passion since childhood for software development. I'm now a PhD student at MILA studying deep learning and it rocks! 👍
Location: Montréal
Blog: https://mila.umontreal.ca/en/person/philippe-paradis
Phil Paradis's Projects
Computes various topological invariants, such as the cohomology of various cdga (commutative differential graded algebra) models in Rational Homotopy Theory. In particular, this program allows tedious computations of the rational retraction index to be automated.
Submission to the CDMC 2015 competition
A text-based learning robot
Differentially Private Gradient Descent Algorithms with some experiments
Domain Specific Classifier in R (soon to be a CRAN package)
Homework from Carleton University course STAT5703W with Shirley Mills
Minimal is the new cool.
IEEE Stream Mining submission for 2015 paper calls
code for the course IFT6266 on deep learning @ UdeM
Final Project for IFT62666 course in Winter 2017 at UdeM
Experiments, models and notes for the project of the IFT6266-H2017 "Deep Learning" course by Aaron Courville.
Study group for IFT6266. Main focus is the final exam, but it it goes well we'll continue meeting to discuss the final project.
Bootstrap 3 fork of jekyll-bootstrap. The quickest way to start and publish your Jekyll powered blog.
Krishna is a minimal Jekyll theme suitable for code based blogs and project showcase.
:triangular_ruler: A flexible two-column Jekyll theme. Perfect for personal sites, blogs, and portfolios hosted on GitHub or your own server.
Models built with TensorFlow
Papers I need to go through or already went through and and any relevant notes, small experiments or attempt at reproducibility, questions and answers, etc.
This is the complete code and my PDF report for Assignment #2 of STAT 5703W (Data Mining), a graduate course offered by Shirley Mills at Carleton University
Personal website and blog built on Jekyll and based on the Hyde template
Stream Mining Classification paper (work in progress)