In this script, I built and trained a deep neural network using TensorFlow and Keras to classify handwritten digits from the MNIST dataset. I used the Functional API to define a flexible architecture with two hidden layers and a softmax output layer for multi-class prediction. The data was preprocessed, normalized, and reshaped before being fed into the model. I compiled it with a cross-entropy loss function and the Adam optimizer, then trained it over several epochs with mini-batches for efficient learning.
Although the model was trained on MNIST, this structure could be easily adapted for real-world business applications, such as classifying scanned forms, digitizing handwritten notes, or processing user input from touchscreen devices. It’s a solid foundation for any image classification task where speed and accuracy are critical.