Deep Learning

MSc Thesis: Multimodal Similarity Learning for Duplicate Product Identification

State of the art models for Similarity Learning are all based on Deep Learning architecture using Siamese Network [Gregory et al., 2015]. They define a feature extraction pipeline that creates a latent representation of input data. This embedding …

How to choose your loss when designing a Siamese Neural Network ? Contrastive, Triplet or Quadruplet ?

Deep Similarity Learning is the training of a deep learning architecture to learn to detect similarity and disimilarity between two inputs (or more). In this article, I presented, studied and compared three of the most popular losses for the task of …

Deep Similarity Learning & Siamese Networks

In this project, I explored deep similarity learning algorithms and their behaviour with different type of data (sequential data, spatial data, multimodal data). For each of these different modalities, I wrote 2 Medium articles detailing the retained method and providing my implementation.

Introduction to Deep Similarity Learning for sequences

Deep Similarity Learning is the training of a deep learning architecture to learn to detect similarity and disimilarity between two inputs (or more). In this article, I focused on similarities between sentences, presenting the theory as well as …

Deep Reinforcement Learning projects

These 3 projects are implementations made for the udacity's nanodegree program, all passed through a reviewer. They contain a small report, gathering my comprehension fo the algorithm as well as details on my implementation and my parameters.

Time Series Land Cover Challenge: a Deep Learning Perspective

In this project, I explored a Time Series of satellite images dataset by building different deep learning classifiers, finding inspiration in paper research in the field of Time Series classification.

Time Series Land Cover Challenge: a Deep Learning Perspective

Time Series Satellite Imagery is the addition of a temporal dimension to Satellite Imagery. We see in this post how a 10-bands satellite imagery takes benefit from this temporal dimension by using combination of unimodal and multimodel neural …

Segmentation Models on artificial moon imagery

In this project, I trained 4 DL segmentation models on an artificial Lunar Dataset to see how they will perform on real moon images from Nasa.