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Convolutional Neural Network to recognize handwritten digits from the MNIST dataset.We began by preprocessing the data, reshaping images, and converting labels to one-hot encoding. Our CNN architecture involved convolutional layers with ReLU activation and MaxPooling for feature extraction, followed by BatchNormalization for stabilization. We added dense layers for classification, including a final softmax layer. After training the model and evaluating its performance on test data, we visualized training and validation accuracy over epochs using matplotlib.