No tf, no pytorch, just math using MNIST dataset

🚀 Mnist learning with Tensorflow and Serving with FastAPI

This project uses autoencoders to denoise MNIST images, aiming to improve handwritten digit recognition by refining classifier training data

Handwritten digits image classification with the MNIST dataset using MultiLayer Perceptron.

Quantum GAN Model to generate images with limited qubits

Simple Neural Network written from scratch in C++ with a real time interactive and visualization demo.

I used the MNIST dataset for the implementation of a handwritten digit recognition app. To implement this, will be using a special type of deep neural network called Convolutional Neural Networks. In the end, I also build a Graphical user interface(GUI) where you can directly draw the digit and recognize it straight away.

Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.

A resource-conscious neural network implementation for MCUs

Thoughts about Artificial Intelligence using the toy dataset MNIST

A zip file containing images for MNIST-M dataset

Hybrid neural network model is protected against adversarial attacks using either adversarial training or randomization defense techniques

This project implements a handwritten digits classifier using PyTorch. The goal is to accurately classify these images into their respective digit classes.

Modelo de IA para Reconhecer Escritas à Mão Personalizadas

A quantum-classical (or hybrid) neural network and the use of a adversarial attack mechanism. The core libraries employed are Quantinuum pytket and pytket-qiskit. torchattacks is used for the white-box, targetted, compounded adversarial attacks.

A quantum anomaly detection approach using the Cirq and Pennylane libraries, designed to detect adversarial attacks on quantum circuits.

GAN and Monte Carlo simulation provides a powerful approach for identifying anomalies in complex datasets.

Experiments on MNIST dataset and federated training using Flower framework

Generative Adversarial Network to generate handwritten digit images similar to the MNIST dataset.