We create two representations of our input features: sparse and dense: 1. In the sparse representation, the categorical features are encoded with one-hot encoding using the CategoryEncoding layer. This representation can be useful for the model to memorize particular feature values to make certain predictions. 2. In the dense representation, the categorical features are encoded with low-dimensional embeddings using the Embedding layer. This representation helps the model to generalize well to unseen feature combinations.