C

CNN

Android implementation of an offline computer vision algorithm for Malaria parasite detection and classification in thick blood smears (see research project).

Réseaux de neurones pour la reconnaissance de l'oral - Classification de langues sur la base de la prononciation du "r" dans différentes langues

Projet personnel de classification des maladies des feuilles d'arbre. Article technique disponible sur mon blog : https://boulayc.gitlab.io/blog/

This project aims to build a Celebrity Look-A-like Model using CNN (Convolutional Neural Networks) based on the VGG (Visual Geometry Group) 16 Model. Users can enjoy a lighthearted exploration of their celebrity doppelgängers. The project integrates image processing, CNN implementation, transfer learning and practical neural network applications, aligning with the course's focus on the practical implementation of Deep Neural Networks.

Face Authenticator

A Face-Mask detection system.

A real-time human counting system with age and gender classification.

Darknet got illuminated by PyTorch ~ Meet Lightnet

Real-time Gender and Age Recognition from Audio

En este trabajo se varían distintos hiperparámetros de una CNN para mejorar la clasificación multiclase de imágenes

java neural networks

A project focused on weather classification using advanced deep learning techniques, specifically leveraging TensorFlow and a custom Convolutional Neural Network (CNN). The project involved the integration of four diverse weather datasets, namely ACDC, MWD, UAVid, and Syndrone, covering various weather conditions, including clear sky, cloudy, rainy, and sunny weather. Developed a custom CNN architecture using TensorFlow's Keras API, incorporating convolutional layers for feature extraction and dense layers for classification.

Just a simple object detection tested on RoboCup simulation but actually used for my own robot grasping capability.

Prediction of age from X-Ray images of hand bones using deep learning models. We used 3 models: Shallow, ResNet50, and InceptionV4. The best result achieved was with a mean absolute error of 10 months using InceptionV4. The preprocessing of data included computer vision techniques like CLAHE filter and reducing channels, and also creativities such as using the Google MediaPipe library to detect hands and crop on them.

Introduction to AI & Data Analysis: Classification of Iris-dataset with classic Perceptron Classification of MNIST-dataset with MLP and CNN

POC of a chess engine to study alphazero on a single PC using deep4j / CUDA

MLP and CNN to classify TinyImage30 dataset. Fine Tuned models. Applied Grad-Cam to identify parts of the image that highly impact the classification based on model convolution gradients. Feature-2-Seq RNN encoder/decoder network trained on the COCO dataset. The produced model is able to predict reasonable captions for provided test images.