D

deep learning

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

Fresh Class🍎, A deep learning web application that classifies 6 types of fruits into 'Good' or 'Bad' quality using MobileNetV2 CNN. Features a Next.js frontend with real-time image classification

This repo will have all resources, labs, data which I use/d on Kaggle Network

Predicting pathogenic potentials of short DNA reads with reverse-complement deep neural networks.

Old functions to play with neural networks. Developed in December 2024

Sistema de inteligencia artificial basado en visión por computadora para la detección y segmentación de latas y botellas

FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer

This code tries to answer the questions: what are the hardest categories to classify in both single label and multi label image classification tasks? Why do these categories present greater difficulty? Could data augmentation change which the worst performing classes are? Are there consistent patterns across both tasks that explain the poorest performing classes? By using LeNet-5, ResNet-18 and DINO ViT deep learning model architectures

Repository of real applications of neural networks coded in Python with TensorFlow/Keras and PyTorch.

Research, code, algorithms and all related to artificial intelligence

Developed a project utilizing Generative Adversarial Networks (GANs) to convert grayscale images to RGB color images. Leveraged deep learning techniques to train the GAN model on a dataset of grayscale and corresponding color images, achieving realistic colorization results. This project demonstrated proficiency in image-to-image translation and advanced deep learning methodologies within the realm of computer vision.

Real-time Gender and Age Recognition from Audio

Controversy quantification of topics on twitter, based on user probability to participate in a controversy topic, using GNN and NLP models.

The aim of this project is to provide an exploratory analysis of Domain Adaptation (DA) techniques in the context of PHM for Bearings fault prognosis, focusing on Health Index (HI) estimation and Remaining Useful Life (RUL) prediction. The adopted dataset is the PRONOSTIA/FEMTO-ST bearings dataset.

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.