Implement Human Activity Recognition in PyTorch using hybrid of LSTM, Bi-dir LSTM and Residual Network Models

The project I produced for the assignment for 'Course 3: Getting and Cleaning Data, of the Data Science Specialization from Johns Hopkins University on Coursera'.

Code for my Master's thesis on Multi‑Task Self‑Supervised Learning for label‑efficient learning. Modular PyTorch framework combining contrastive + pretext tasks with dynamic loss weighting, and centralized/federated training (HAR/STL‑10) to learn compact, robust representations.

This project is to build a model that predicts the human activities such as Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing or Laying using readings from the sensors on a smartphone carried by the user.

Mini-Project Given during the Assignment 1 of ML Course of IITGN ES-335