Skin Cancer Classification
-
Updated
Jul 6, 2023 - Jupyter Notebook
Skin Cancer Classification
Deploy CNNs model with four FLIR camera synchronously streaming target image
Text classification using CNNs
This repo contains my very first projects in image processing, including school assignments homeworks and other personal projects.
MIT cs231n DeepLearning in CV Assignment
Gesture Recognition model developed using multiple Neural Network based algorithms which can identify 5 different gestures for operating smart TVs.
Various CNN's trained with the Kaggle Chest X-Ray dataset.
Included AI-Projects under Computer Vision.
Visualization methods to interpret CNNs and Vision Transformers, trained in a supervised or self-supervised way. The methods are based on CAM or on the attention mechanism of Transformers. The results are evaluated qualitatively and quantitatively.
Some mini projects and training code
In this repository, a very informative and comprehensive implementation of ConvMixer architecture is provided for educational purposes using PyTorch.
Implementation of CNN (Convolutional neural network) from scratch
Implementações de Redes Neurais Artificiais (RNAs), incluindo algoritmos de classificação e regressão, e Redes Neurais Convolucionais (CNNs).
Classification of Fashion-MNIST dataset using both Dense ANNs and CNNs
UDS (Udacity Driverless System) is an autonomous driving project developed using deep learning techniques and socket communication with a simulator. It was developed as an internship project during my internship at Zhilin Information Technology Co., Ltd., organized by Taiyuan University of Technology and the company itself, in 10.04.2024-23.04.2024
Code for submission of Speiser et al to spike-finder-challenge 2017
Thoracic Disease Detection Using CNNs and Weighted Binary Cross Entropy Loss Based on Chest X-Ray Images
Explored calibration in Convolutional Neural Networks (CNNs) using the CIFAR-10 dataset, focusing on binary classification of birds and cats. The project encompasses data preprocessing, model training, and evaluation, with a deep dive into calibration techniques. Weight & Biases library for monitoring training processes and model performance.
Add a description, image, and links to the cnns topic page so that developers can more easily learn about it.
To associate your repository with the cnns topic, visit your repo's landing page and select "manage topics."