A zip file containing images for MNIST-M dataset
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Updated
May 22, 2024
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
vanilla GAN on the MNIST dataset. There is room for improvement in terms of generator's architecture and training
improved vanilla GAN model. it's probably overfitting
This project implements a handwritten digits classifier using PyTorch. The goal is to accurately classify these images into their respective digit classes.
A resource-conscious neural network implementation for MCUs
Classificação de imagens de dígitos escritos à mão do dataset MNIST usando um MultiLayer Perceptron.
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.
Example of containerized ML workflow using MNIST dataset
Example of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)
The goal of this project was to help me in learning more about neural networks and how ai and machine learning algorithms use deep learning.
Implementations of neural networks in python for the classification of MNIST datasets.
Hamming Network implementation using PCA implementation from scratch
GenAI-EvenMNIST harnesses generative AI through a Variational Auto-Encoder (VAE) with convolutional layers to generate even-digit images from the MNIST dataset. This project showcases the application of AI for image creation and deep learning
different machine learning works on MNIST dataset
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