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.

CNN applied on Cifar-10 database.

This repository contains a collection of projects focused on implementing various deep learning models and algorithms on FPGA. These projects leverage the power of FPGA for efficient and high-performance execution of machine learning tasks.

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.

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

This project implements and tests Convolutional Neural Network (CNN) models to classify images from the CIFAR-10 dataset, which includes 60,000 color images across 10 classes. The models achieve up to 90.45% accuracy, with training stability considerations and evaluation through confusion matrices and training history.

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.

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

This project demonstrates image classification using a Convolutional Neural Network (CNN) on the CIFAR-10 dataset. The model is trained to classify images into one of 10 classes.

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

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

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…