Deep Learning with PyTorch

Overview

This repository contains various deep learning projects implemented using PyTorch. Each project follows a structured approach to ensure effective model development and evaluation.

Project Structure

1. Data Visualization & Pre-Processing:

   • Explore and clean the data.
   • Handle missing values and normalize features.

   

2. Model Architecture:

   • Design and select the appropriate model.
   • Define layers and activation functions.

   

   

3. Model Training:

   • Train the model using gradient descent.
   • Adjust hyperparameters and monitor performance.

   

4. Model Evaluation:

   • Assess the model’s accuracy and generalization on test data.

   

5. Outliers Handling:

   • Detect and manage outliers to ensure model robustness.