📊 Deep Learning Experiment Tracking with PyTorch

📊 Deep Learning Experiment Tracking with PyTorch

Project Overview

This project demonstrates how to track and monitor deep learning experiments using PyTorch and TensorBoard.

The notebook focuses on building reproducible deep learning workflows, training neural network models, and visualizing training metrics to better understand model performance.

Features

• Deep learning model training with PyTorch
• Experiment tracking
• TensorBoard integration
• Performance monitoring
• Training visualization
• Model evaluation

Technologies Used

• Python
• PyTorch
• TorchVision
• TensorBoard
• NumPy
• Matplotlib
• Jupyter Notebook

Project Structure

deep-learning-experiment-tracking/

├── experiment_tracking.ipynb
├── README.md
├── requirements.txt
└── .gitignore

Learning Objectives

This project demonstrates:

• Building deep learning models with PyTorch
• Tracking machine learning experiments
• Monitoring training performance
• Comparing experimental results
• Visualizing metrics using TensorBoard

Installation

Clone the repository:

git clone https://github.com/TOkYOOO0/deep-learning-experiment-tracking.git

Install the required packages:

pip install -r requirements.txt

Launch Jupyter Notebook:

jupyter notebook

Author

Hamed Wahedi

Machine Learning & AI Enthusiast