How are deep learning models built on Keras?

Deep learning models in Keras are built using a modular approach, allowing developers to construct layers sequentially or using a functional API for more complex architectures. Layers such as convolutional, pooling, and dense layers are stacked together to form a neural network. Developers define key parameters like activation functions, optimizers, and loss functions. After compiling the model, they train it on datasets using the fit() method and evaluate its performance with evaluate(). This workflow simplifies the development of complex models while offering flexibility for customization.