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.

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 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.