Developers: Lachanas Dimitris, Tilikidou Sofia, Zilachovinos Apostolos
This repository implements the second lab exercise for the "Neural Networks" course. It focuses on using recurrent neural networks to solve sequence-to-sequence problems, specifically comparing RNN, LSTM, and GRU architectures for estimating the power consumption of a specific appliance based on total household consumption.
Objective: Develop and compare three architectures: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on the "Coffee Machine Consumption" dataset. Dataset: Includes files for total and appliance-specific power consumption.