Rendering a flask app for hand-written digits recognition.
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Updated
May 1, 2023 - HTML
Rendering a flask app for hand-written digits recognition.
MNIST based Handwritten Digit Recognition using Deep Learning
Project for the course WIA1007 (Intro to Data Science) in University of Malaya
Web application written in Python using flask and deployed on Heroku. The purpose of this project was to connect a UI with 2 of the ML models I have developed for recognizing handwritten numbers.
This is my deep learning repository where I applied some deep learning state-of-the-art techniques and architecture to the dataset.
Neural Network is trained on infamous MNIST dataset which recognizes handwritten digits using Deep Learning. Implemented using Pytorch
Made with the ONNX.js framework from Microsoft. Similar to tensorflow.js, ONNX.js is another framework to provide the capability of running machine learning models on the web with JavaScript
Our project builds a Convolutional Neural Network (CNN) model to accurately classify handwritten digits from the MNIST dataset. We preprocess the data and design a CNN architecture. Additionally, we create a user-friendly web interface using Flask for easy digit classification.
PCA in MNIST to understand handwriting patterns
Generazione di Immagini Avversariali in Tensorflow
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