Deep Neural Network for Handwritten Digit Recognition using MNIST Dataset: Achieving 98.43% Accuracy
This repository contains the code for building a deep neural network model to recognize handwritten digits from the famous MNIST dataset. The MNIST dataset consists of 28x28 grayscale images of handwritten digits (0-9) along with their corresponding labels.
Utilizes the Keras library to create a deep neural network for digit recognition. Achieves an accuracy of 98.43% on the test dataset. Implements data preprocessing techniques such as normalization and one-hot encoding. Includes visualization of training loss and accuracy for model evaluation. Provides code to display a few sample images from the dataset. The model architecture consists of multiple layers of densely connected neurons, using the Rectified Linear Activation (ReLU) function for hidden layers and the Softmax activation function for the output layer. The Adam optimizer is used to optimize the model's weights and biases during training.
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