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Handwritten Digit Recognition 📊 🖋️

The Handwritten Digit Recognition project is a machine learning-based application developed in Python that can accurately identify and classify handwritten digits. This project utilizes the popular MNIST dataset, consisting of thousands of hand-drawn digits, to train a Convolutional Neural Network (CNN) model.

Key Features: ✨

• Trained a robust CNN model for accurate digit recognition. 🧠
• Preprocessed the dataset, including data normalization and one-hot encoding. 📈
• Designed the CNN architecture with convolutional and pooling layers for feature extraction and downsampling. 📷
• Implemented dropout layers to prevent overfitting during training. 🚫
• Utilized the Adam optimizer and categorical cross-entropy loss function for efficient model training. ⚙️
• Incorporated early stopping and model checkpointing for improved training performance. 🏁
• Achieved a high accuracy rate of 98.8% on the test dataset for handwritten digit recognition. 👏

This project serves as a demonstration of proficiency in deep learning, computer vision, and model evaluation. It showcases the capability to build and train a machine learning model for practical image recognition tasks. 🌟