Machine Learning Theory
Generalization, optimization, implicit bias, grokking, and statistical learning foundations.
Teaching
Teaching
Tutorials, reading notes, and course-style material.
Mathematical Tools
Probability, linear algebra, stochastic processes, sampling, and dynamical systems viewpoints.
Deep Learning Practice
Representation learning, language models, embeddings, vector quantization, and research code habits.
Past Teaching
Mentoring & Tutoring
Université de Montréal
Tutor for undergraduate students in Computer Science and Maths
Courses: Foundations of Machine Learning, Statistical Learning, Simulation and Modeling, Probability and Statistics, Calculus, Linear Algebra.
Yaounde
Preparatory classes
During my training as an engineer, I prepared many students in mathematics and physical sciences for the entrance exams of the Grandes Ecoles in Cameroon.
Yaounde
Group and home rehearsal courses
During my engineering training, I gave tutoring in mathematics, physics and chemistry to college students, at home (private) and in group.
Tutorial Repository
Current Notes
April 29, 2022
Intrinsic Dimension Estimation
Tensorized and parallelizable pytorch implementation of the algorithm for intrinsic dimension estimation.
April 14, 2022
Generating Random Variables and Stochastic Processes, Generative Flow Networks (GFlowNets)
Practical tutorial about GFlowNets, MCMC, Metropolis-Hasting, Gibbs sampling, and related stochastic simulation methods.
March 1, 2022
Pre-train and Fine-tune a Language Model with 🤗 Transformers
My tutorial on how to pre-train a causal and masked language model with the Hugging Face transformers library.
August 7, 2020
Word embeddings
My original tutorial on word embeddings, from Bag of Words and TF-IDF to Word2Vec and GloVe.