samantaheri71

Dynamic-Line-Forecasting

a new stacked bagging boosting ensemble is developed based on multivariate empirical mode decomposition.

Saman

LSTM-Models-FDD-task

Here we share our experiments regarding training different DRNN configurations on the fault detection diagnosis of HVACs. The repository contains a fully-developed pipeline for data preprocessing of a common HVAC datasets, as well as different file configurations used in the paper. The objective was to optimize the associated hyperparameters, which is a very difficult task.

LSTM-PAPER

Here we share our experiments regarding training different DRNN configurations on the task of long-term energy consumption prediction. The repository contains a fully-developed pipeline for data preprocessing of a common energy consumption dataset, as well as different file configurations used in the paper. The objective was to optimize the associated hyperparameters, which is a very difficult task.

Tom-Mitchell-s-Facial-Recognition-Python-Code

I was reading the great Tom Mitchel's book on machine learning and I came across one of its examples which were about facial recognition using artificial intelligence. The book has provided us with C++ code, yet I thought it would be useful to write the code in Python. (I searched but could not find a Python code) So I wrote the code, and I have assumed that python will do nothing special for backpropagation learning and I wrote the code from scratch. In this manner, the code is longer and maybe harder to read. Yet, it shows a beginner how exactly the backpropagation works. Also, you should try different hyper-parameters in the code ( learning rate and epochs) to achieve desirable results.