This project demonstrates a simple pipeline for generating various modulation signals like QPSK, 64QAM, PAM4 etc, training a lightweight neural network to classify them, converting the model to TensorFlow Lite format for edge deployment, and running inference using the TFLite interpreter.

On Device ML Model inference using Tensorflow Interpreter, Heart Attack Risk Predictor[HARP] is designed to work with and without internet, when there is no internet, the model inference is achieveing by invoking Tensorflow interpreter

A minimal TensorFlow project that trains a simple linear regression model, converts it to TensorFlow Lite, and runs inference using the TFLite interpreter. Ideal for learning model deployment to mobile or edge devices.