Implementation-of-Some-Papers-Related-to-Neural-Networks-and-Deep-Learning

This repository contains Python implementations of various papers that I have studied during my Neural Networks and Deep Learning course. These implementations were part of the course homework assignments. I plan to implement more papers related to this field in the future. Therefore, I will be updating this repository periodically so that you can access all my paper implementations. The papers and models I’ve implemented so far include:

1. Mcculloch-Pitts, Adaline, Madaline, multi-layer perceptron, transfer learning, and clustering using a deep autoencoder, based on this paper

2. Preprocessing, data augmentation, and solving the Face emotion recognition problem using CNN, AlexNet, VGGNet, and MobileNet, based on this paper

3. Faster RCNN for object detection, based on this paper

4. LSTM, GRU, Bi-Directional LSTM, MLP, CNN, Conv-LSTM, and Naive forecast for time series prediction, based on this paper

5. Persian speech emotion recognition using HuBERT, based on this paper

Note: You may find identical code to sections 3 to 5 here. This is because the course homework assignments could be completed in pairs, and the individual I mentioned was my partner for those assignments. Each of us implemented a completely different paper. However, our collaboration has since ended, and as a result, I have not included his code here. Despite this, he has uploaded my code on his own GitHub page without crediting me!