Convolutional Neural Network (CNN) – PyTorch Learning

This project serves as an educational resource for learning how to build, train, and evaluate convolutional neural networks (CNNs) using PyTorch.

Table of Contents

• About
• Features
• Requirements
• Installation
• Usage
• Project Structure
• Resources
• License

About

This repository provides sample code and Jupyter notebooks demonstrating how to implement CNN architectures with PyTorch. The focus is on helping users understand the fundamentals of deep learning for computer vision.

Features

• PyTorch-based CNN implementation
• Example code for data loading and preprocessing
• Model training and validation workflow
• Visualization of training performance metrics
• Modular and customizable notebook structure

Requirements

• Python 3.7+
• PyTorch
• Jupyter Notebook
• Numpy
• Matplotlib

Installation

Clone the repository:

git clone https://github.com/dctn/convolutional-neural-network.git

cd convolutional-neural-network

Then install dependencies (recommend using a virtual environment): pip install torch torchvision notebook numpy matplotlib

Usage

Start Jupyter Notebook in the project directory: jupyter notebook

Open the provided .ipynb notebooks and follow the instructions to train CNNs on sample data.

Project Structure

convolutional-neural-network/
├─ README.md
├─ cnn_example.ipynb
├─ utils.py
├─ requirements.txt
└─ data/
   └─ (Put your dataset here)

Resources

• PyTorch Documentation
• Deep Learning with PyTorch

License

This project is licensed under the MIT License.