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This web app classifies clothing items from the Fashion MNIST dataset using a neural network that I trained. Users can explore random images from the test set or upload their own images for classification.
Creating neural network from scratch with just basic python libraries such as cupy (numpy on crack) without any DL module like tensorflow, pytorch or sklearn.
This project implements a Convolutional Neural Network (CNN) to classify handwritten digits from the MNIST dataset. The model is built using TensorFlow and Keras.
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 comprehensive MNIST digit recognition project with a Streamlit dashboard, neural network model, and Jupyter notebook. Includes all necessary files for training, testing, and deploying the model.