How are deep learning models built on Keras?

In Keras, you build deep learning models by adding layers one at a time or using the functional API for advanced designs. You can stack layers such as convolutional, pooling, and dense. Developers choose settings such as activation functions, optimisers, and loss functions. After setting up the model, you compile it, train it with the fit() method, and evaluate its performance using evaluate(). This step-by-step approach makes setting up and customising models simple and clear.