In this project, I have built a convolutional neural network in Keras with Python on a CIFAR-10 dataset. First, we will explore our dataset, and then we will train our neural network using Python and Keras. After training the model and obtaining the suitable accuracy we finally conclude our model creation part. Next, we have used Tkinter library…

This GitHub repository hosts my comprehensive CIFAR-10 image prediction project, which I completed as part of the SmartKnower program. CIFAR-10 is a widely used dataset in computer vision, consisting of 60,000 32x32 color images from 10 different classes.

This repository contains my final submission for the COMP3547 Deep Learning module assignment at Durham University in the academic year 2022/2023. The project focuses on diffusion-based models and their application in synthesising new, unique images, which could plausibly come from a training data set. Final grade received was 71/100.

In this we use Bayesian Statistical principles to classify images present in 10 different clases such as airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck.

Project related to my thesis which focuses on Continual Learning carried out on the CIFAR10 dataset.

Regular Neural Network and Convolutional Neural Network were applied on Cifar - 10 database, and checked the final results.

This repository is used for "Computer Vision and Pattern Recognition" course work.

CNN applied on Cifar-10 database.

In this project we will build multiple CNN models for CIFAR-10 Image Classification