https://github.com/sudarshanasrao/ee541-deep_learning-usc

USC graduate level Deep Learning course
https://github.com/sudarshanasrao/ee541-deep_learning-usc

cnn deep-learning mlp-networks neural-networks numpy python pytorch scipy

Last synced: about 1 month ago
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USC graduate level Deep Learning course

• Host: GitHub
• URL: https://github.com/sudarshanasrao/ee541-deep_learning-usc
• Owner: SudarshanaSRao
• License: mit
• Created: 2022-10-02T17:06:51.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-01-02T18:01:11.000Z (about 2 months ago)
• Last Synced: 2025-01-02T18:41:01.556Z (about 2 months ago)
• Topics: cnn, deep-learning, mlp-networks, neural-networks, numpy, python, pytorch, scipy
• Language: Jupyter Notebook
• Homepage:
• Size: 2.38 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# USC EE541-Deep-Learning

Welcome to the repository for my **Deep Learning (EE541)** assignments! All the assignments in this repo were coded using **Python**. Below, you'll find the relevant files for each assignment, including datasets and Jupyter notebooks.

## Introduction

This repository contains my solutions to the **Deep Learning (EE541)** assignments. These assignments cover various deep learning concepts, including neural networks, CNNs, ANNs, multi-layer perceptrons, clustering, and unsupervised learning, using popular frameworks/libraries like **PyTorch**, **scikit-learn**, **NumPy**, etc.

## Assignments

### Assignment 1.3

- **Files**:

  - `func.py`: Contains the main functions for this assignment.

  - `EE541_HW-1_3.ipynb`: A Jupyter notebook for Assignment 1.3 with implementation and explanations.

  

This assignment involves building and training deep learning models for a specific task (e.g., classification, regression). The notebook includes detailed explanations of the model architecture, data preprocessing, and evaluation metrics.

### Assignment 2.1

- **Dataset**: `raman.txt`

  

This assignment uses the **raman.txt** dataset, which contains data for clustering tasks. The focus is on applying clustering algorithms like K-Means or hierarchical clustering to group the data based on its features.

### Assignment 2.2

- **Dataset**: `cluster.txt`

  

This assignment uses the **cluster.txt** dataset, where the task is to apply clustering techniques (e.g., K-Means) to categorize data points into clusters. The focus is on feature extraction, dimensionality reduction, and cluster evaluation.