Deep Learning Projects

This repository contains several deep learning projects, each focusing on different applications and models within the field of deep learning.

Table of Contents

• Project 1: Clustering with SOM, Classification with SLFN
• Project 2: Recurrent Neural Networks in the World of Stocks
• Project 3: Blind Source Separation Using Variational Autoencoders
• Project 4: Persian News Classification Using LSTM+Attention

Project 1: Clustering with SOM, Classification with SLFN

• Task:
  • Part I: Use Self-Organizing Maps (SOM) for clustering countries based on coronavirus cases.
  • Part II: Implement a Single Layer Feed-forward Network (SLFN) for classification tasks.
• Model:
  • SOM for unsupervised clustering.
  • SLFN for classification.
• Dataset:
  • Coronavirus cases data for clustering.
  • A provided dataset for classification.

Project 2: Recurrent Neural Networks in the World of Stocks

• Task:
  • Use simple RNNs (Elman or Jordan networks) to predict the Tehran Stock Exchange Index based on historical price data.
• Model:
  • Simple RNNs (Elman or Jordan networks).
• Dataset:
  • Four years of historical data for the Tehran Stock Exchange Index.

Project 3: Blind Source Separation Using Variational Autoencoders

• Task:
  • Part I: Use Variational Autoencoders (VAEs) to separate mixed MNIST and Fashion MNIST images.
  • Part II: Use VAEs to separate vocal and background music components from audio recordings.
• Model:
  • Variational Autoencoders (VAEs).
• Dataset:
  • MNIST and Fashion MNIST for image separation.
  • IRMAS dataset for music separation.

Project 4: Persian News Classification Using LSTM+Attention

• Task:
  • Implement an LSTM model with an attention mechanism to classify a Persian news dataset into different categories.
• Model:
  • LSTM with attention mechanism.
• Dataset:
  • Persian news dataset containing titles, summaries, and content of news articles across six categories.