final_project.md

🧠 Simple Summary of the Program

1. Loads and Prepares Data:

   • Uses the MNIST dataset, which contains images of handwritten digits (0-9).
   • Resizes the images and converts them to tensors.
   • Creates a data loader to batch the images and shuffle them for training.

2. Defines a CNN Model:

   • The FinalCNN model processes the images through layers:
     • Conv1: Finds simple features like edges.
     • Pool1: Reduces the size to focus on important features.
     • Conv2: Finds more complex patterns.
     • Pool2: Reduces the size again.
     • Flattening: Converts the features into a single line of numbers.
     • Fully Connected Layers: Makes predictions about what digit is in the image.

3. Trains the Model:

   • Uses the Cross-Entropy Loss to measure how far the predictions are from the real digit labels.
   • Uses Stochastic Gradient Descent (SGD) to adjust the model parameters and make better predictions.
   • Runs the training for 32 epochs, slowly improving the accuracy.

4. Displays Predictions:

   • Shows 6 sample images with the model's predictions and the actual labels.
   • Prints the accuracy and loss for each epoch.

5. GPU Acceleration:

   • Uses CUDA if available, making the training faster by running on the GPU.

✅ This program is like a smart detective that learns to recognize handwritten numbers by studying lots of examples and gradually improving its guesses.