Recommender Systems: An Applied Approach using Deep Learning - Two-Tower Model

The video tutorial explains the two tower model used in Tensorflow for recommendation systems. It covers user and item embeddings, features, and how they are processed through separate neural networks. The training and learning processes are discussed, followed by combining the networks to make predictions about user preferences. The tutorial concludes with an overview of the project's implementation using this model.

Explain how the output layer contributes to making predictions in the two tower model.

What are the key elements included in the project that utilizes the two tower model?