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Overview:

This project focuses on recognizing handwritten digits using a neural network. The system is trained using data from the Keras datasets, and the model is designed to classify images of numbers. It uses layers like ReLU and Softmax for activation, MaxPooling for dimensionality reduction, and Dropout to prevent overfitting. The model is trained over 20 epochs with a batch size of 50.

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Key Features:

1. Neural Network Architecture:

The model utilizes a combination of ReLU and Softmax activations, Dense(256) layers for classification, and MaxPooling2D for reducing dimensionality. Dropout(0.5) is applied to prevent overfitting during training.

2.Training Process:

The model is trained over 20 epochs with a batch size of 50, optimizing its ability to classify handwritten digits effectively from the training dataset.

3. Performance Metrics:

The model measures accuracy and loss on both training and testing data to ensure it generalizes well and performs accurately in recognizing handwritten digits.

4. Keras Datasets: Pre-loaded data from Keras is used, providing a robust and reliable set of handwritten digit images for training and validation.

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