Deep Learning Foundation Using PyTorch and PyTorch Lightning

Project Overview

This project explores deep learning concepts and implements them using PyTorch and PyTorch Lightning. The notebook provides a structured workflow for building and training neural networks, focusing on modular and efficient code.

Contents of the Project:

1. Data Handling:

   • Loading and preprocessing datasets for training and validation.
   • Implementation of custom datasets and data loaders using PyTorch.

2. Model Architecture:

   • Definition of neural network models with PyTorch.
   • Use of PyTorch Lightning to abstract model training and evaluation.

3. Training and Validation:

   • Implementation of training loops and validation steps using PyTorch Lightning's Trainer.
   • Configuration of optimizers, learning rate schedulers, and callbacks.

4. Evaluation:

   • Model performance analysis using metrics and visualizations.
   • Comparison of training and validation results to assess overfitting or underfitting.

5. Experimentation:

   • Modifications to hyperparameters, architectures, and training configurations to observe their impact on performance.

This project demonstrates a complete deep learning pipeline with practical applications of PyTorch and PyTorch Lightning for efficient and scalable model development.