Text Summarizer Flask Application is a web app that uses a BART-based model to summarize user-provided text. It stores inputs and summaries in a SQLite database and includes functionality to fine-tune the model with stored data.

Fine-tune YOLOv9 for custom object detection with this step-by-step guide, including dataset preparation, training, validation, and inference tools.

An industry-focused guide for building, deploying, and optimizing generative AI applications, incorporating advanced techniques such as RAG, model fine-tuning, and scalable cloud/on-premise deployment strategies.

Torch and Transformers Playground: Learn and Code Deep Learning using PyTorch and HuggingFace Transformers.

A transfer learning project using MobileNet and the Alpaca Dataset for image classification. Includes data preprocessing, model fine-tuning, evaluation, and deployment. Demonstrates efficient computer vision workflows with small, specialised datasets.

GatorSched harnesses GPT-2 to intelligently parse and respond to queries from lifelog data, providing tailored recommendations and insights for effective scheduling

Powerful Python tool for visualizing and interacting with pre-trained Masked Language Models (MLMs) like BERT. Features include self-attention visualization, masked token prediction, model fine-tuning, embedding analysis with PCA/t-SNE, and SHAP-based model interpretability.

🪘 Tabla Drum Image Generator – AI-powered tabla drum image generation using Stable Diffusion & GANs. Features custom dataset curation, ML training pipeline, and scalable API deployment.

L'objectif de ce challenge est de proposer une méthode de classification, basée sur des réseaux de neurones, permettant de classer des images issues de Google Quickdraw (https://quickdraw.withgoogle.com/data). Le jeu de données proposé comprend 5 classes balancées, avec 15000 exemples d'apprentissage et 5000 exemples de validation.