Barbados Traffic Analysis Challenge
ML solution for predicting traffic congestion using video analysis. Implemented YOLO detection, VideoMAE embeddings, and ensemble models.
Everything I have built and can show, newest first. Most are open on GitHub; the rest are client systems I can describe but not link.
y = φ( ∑i wixi + b )
[one artificial neuron, Rosenblatt 1958, The Perceptron]
One artificial neuron: it multiplies each input by a weight, adds a bias b, and passes the sum through an activation φ to produce its output. On its own it can only split its inputs with a linear boundary, so it cannot learn XOR, the limit Minsky and Papert made famous in 1969. Stacked in layers, units like this one overcome that limit, and they are the building block of the deep networks behind many of the projects below. Here the three inputs are the fields I work in most: vision, language and speech.
ML solution for predicting traffic congestion using video analysis. Implemented YOLO detection, VideoMAE embeddings, and ensemble models.
Python project for extracting and processing data from Spotify Web API with OAuth 2.0 authentication and pagination strategies.
Open-source toolkit to collect, preprocess, align, and normalize Mooré language speech/text data for low-resource NLP applications.
ETL pipelines and Deep Learning models to forecast droughts from satellite data, helping Moroccan farmers optimize water resources.
Unsupervised clustering of gene expression data using K-Means and DBSCAN for automatic tumor type identification from TCGA dataset.
Real-time streaming mobile assistant using Computer Vision and Speech processing to help visually impaired users navigate their environment.
2nd place solution at the pan-African Data Science Nations Cup. Developed regression models to predict energy demand across 50+ African countries.
Scripts and utilities for fine-tuning Meta’s Massive Multilingual Speech (MMS) adapter models for ASR on low-resource languages like Mooré.
Comprehensive collection of notebooks for Microsoft’s Florence-2 VLM: inference, data auto-labeling, and fine-tuning for various computer vision tasks.
Deep learning model for classifying radio signals using spectrogram images with PyTorch and transfer learning.
Deep Convolutional GAN implementation in PyTorch for generating realistic handwritten digits from the MNIST dataset.