This repository contains the projects I completed for the Neural Networks and Deep Learning course at the University of Tehran.
Getting Started
- This repository assumes you have Python and a deep learning framework like TensorFlow, PyTorch, or Keras installed.
Contents
This repository includes folders for each project. Each folder will likely contain:
- Jupyter notebooks or Python scripts implementing the project.
- Data files used for training and testing (if applicable).
- README.md file (optional, for project-specific details).
Project Descriptions
1. Neural Networks Basics
This project provided a foundational understanding of neural networks
2. Multi Layer Perceptron (MLP)
Building upon the basics, this project focused on Multi-Layer Perceptrons (MLPs).
3. CNN, Augmentation and Transfer Learning
This project delved into Convolutional Neural Networks (CNNs), a powerful architecture for image recognition.
4. YOLO
This project focused on YOLO (You Only Look Once), a state-of-the-art real-time object detection system.
5. Time Series
This project explored Recurrent Neural Networks (RNNs) and their variants, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). These networks excel at handling sequential data Applications in tasks like time series forecasting and sentiment analysis
6. Generative Networks
This project introduced generative models, a fascinating area of deep learning that can create new data:
- Generative Adversarial Networks (GANs) and their training process
- Variational Autoencoders (VAEs) for learning data representations
7. Memory Networks
This project might have covered memory networks, a less common architecture but with interesting capabilities:
8. Some Other Networks
This project might have allowed you to explore other interesting Neural Networks beyond the ones covered previously.