Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)
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Updated
Oct 17, 2023 - Python
Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)
Basic Gesture Recognition Using mmWave Sensor - TI AWR1642
Tensors and dynamic Neural Networks in Mojo
2D Convolutional Recurrent Neural Networks implemented in PyTorch
Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images
Mokka is a minimal Inference Engine for Dense and Convolutional 2D Layer Neural Networks. Written on a single C++ header, it uses AVX2
A project to perform people identification at a distance using face and gait data with deep learning
A project to perform people identification at a distance using face and gait data with deep learning
This PyTorch-based project implements a deep neural network for multi-class classification of fashion items. The dataset consists of images categorized into three classes: glasses vs. sunglasses, shoes, and trousers vs. jeans.
This code uses the pyTorch Conv2D modules to make the PIV algorithms work faster on GPU
Basic_CNN_Implementation
Image classifier application to classify flowers to 102 categories, using TnensorFlow hub and Conv2D
A repository for machine learning problems and exploration of different ML libraries. The goal of this repository is to collect takeaways while developing ML models. This should improve my overall understanding of developing machine learning applications.
Handwriting digit recognition using keras.Conv2D and MNIST database.
Creating a classifier for the German Traffic Signs dataset that classifies images of traffic signs into 43 classes.
This project is to apply Convolutional Neural Networks (CNN) to recognize dog breeds.
Image classification based computer vision model CNN
This repository Investigates DCGAN using facedata. Serves as a personal cautionary tale when working with GANS.
Basic_CNN_Implementation
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