Neural Networks and Deep Learning Course Project 🧠

Welcome to my Neural Networks and Deep Learning course project repository! This project was completed as part of my coursework with Dr. Ahmad Kalhor at the University of Tehran, and it features six different assignments that cover a variety of topics in the field of deep learning.

Assignments 📝

Here's a breakdown of the different assignments that are included in this project, along with two representative images of the same size for each one:

CA1: Adaline, Madaline, RBM, and MLP

This assignment explores the performance of different neural network architectures, including Adaline, Madaline, RBM, and MLP.

CA2: Convolutional Neural Networks (CNN)

In this assignment, we implement a CNN for image classification and compare its performance with other algorithms.

CA3: Transfer Learning, Segmentation, and YOLOv5

This assignment focuses on transfer learning and implementing segmentation using YOLOv5.

CA4: Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN), CNN-LSTM

In this assignment, we dive into the world of RNNs and LSTM architectures, and we combine them with CNNs.

CA5: BERT Implementation, BEIT for Image Segmentation and Classification

This assignment focuses on implementing the BERT model for natural language processing tasks and BEIT for image segmentation and classification.

CA6: Deep Conditional Generative Adversarial Networks (CGAN), Auxiliary Classifier GAN (ACGAN), Wasserstein GAN

In this assignment, we explore various GAN architectures, including Deep CGAN, ACGAN, and Wasserstein GAN.

Contact Information 📬

If you have any questions or feedback about this project, I'd love to hear from you! You can reach me at:

• Email: [email protected], [email protected]

Thanks for stopping by! 😊