🚀 Project Overview 🚀

This project is a Handwritten Digits Classifier implemented using PyTorch, a powerful deep learning library. The classifier aims to accurately predict the digit represented by an input image of handwritten digits.

📚 Architecture 📚

The architecture of the Handwritten Digits Classifier utilizes a Transformer Compose pipeline, which consists of several layers for feature extraction, transformation, and classification. This model is designed to effectively process and analyze input images to predict the corresponding digit.

🔍 Loss Function 🔍

The loss function employed in this project is the CrossEntropyLoss function. Cross-entropy loss is commonly used in classification tasks, as it calculates the difference between the predicted probability distribution and the true distribution of the labels.

⚙️ Optimizer ⚙️

The optimizer used for training the model is the Adam optimizer. Adam is an adaptive learning rate optimization algorithm that efficiently updates the model's parameters during training. It is well-suited for deep learning tasks due to its ability to handle sparse gradients and noisy data.

🎯 Accuracy 🎯

The accuracy achieved by the Handwritten Digits Classifier is an impressive 97.42%. This accuracy rate reflects the effectiveness of the model in accurately classifying handwritten digits.

📝 Usage 📝

To use the Handwritten Digits Classifier, follow these steps:

1. Prepare the dataset: Ensure that you have a dataset of handwritten digits images for training and testing the model.
2. Preprocess the data: Preprocess the images to prepare them for training, including normalization and resizing if necessary.
3. Train the model: Use the provided PyTorch scripts to train the model on the prepared dataset.
4. Evaluate the model: Evaluate the trained model on a separate test dataset to assess its accuracy and performance.

🎉 Happy Classifying! 🎉