Simpson character classification

Trained a multiclass classifer network using cifar100 dataset

This project is Multi-class Image classification using Convolutional Neural Network developed using Python programming language.

Multiclass image classification of Bark-50 texture data from https://www.kaggle.com/datasets/saurabhshahane/barkvn50

Sequential CNN model using tensorflow for multi-class classification on images.

Ths project explains how a bigener can use simple convolutional neural network (cnn) to classify car images

Multi Class Classiffication with Convolutional Neural Network(CNN) using Keras on weather photos

Successfully trained a deep learning model which can precisely predict the species of flowers based on their images.

Object Detection using CNN Algorithm using kaggle dataset

SSCI23 Explainable AI in Network Traffic Classification

This project was part of the MLx Cases section of the OxML 2023 summer school.

BME499 course materials. I developed the neural networks / deep learning section of this course.

Multi-class classification of footwear images using a convolutional neural network. Dataset and trained model available

Convolutional Neural Network(CNN) using Python and Keras to classify American Sign Language (ASL) alphabet hand signs.

Multiclass classification of images of cats, dogs and fish

Project implementation of land cover classification problem. This repository contains the implementation of models in pytorch lightning and their results.

Misdiagnosis of many diseases affecting agricultural crops can lead to chemical misuse resulting in the emergence of resistant pathogenic strains, increased input costs, and more outbreaks leading to significant economic losses and environmental impacts. A structure of a convolutional neural network has been proposed that is capable of diagnosin…

Multi-class classification of German traffic signs with deep convolutional neural networks. Architecture inspired by LeNet.

This is a numpy implementation for the shallow neural network algorithm (both training and testing) fully vectorized