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In this project, a sophisticated deep learning model called Convolutional Neural Network in conjunction with Dense Network (~20 million parameters) was utilized for predicting human handwritten digits with 98% accuracy with only 5 training epochs. The front end was designed to allow user to input their handwritten on a canvas and the machine wil…
This repository contains the code for building a spam detection system for SMS messages using deep learning techniques in TensorFlow2. Three different architectures, namely Dense Network, LSTM, and Bi-LSTM, have been used to build the spam detection model. The final model has been deployed as a Streamlit app to showcase its working.