The main idea behind the Keras development is to facilitate experimentation's by fast prototyping. The is great to go from an idea to result with the least possible delay is key to good research. The structure in Keras is the Model that defines the complete graph of a network. To create a custom model for a project, we simply add more layers to the existing model.
Let's look at the model architecture in Keras in the following screenshot:
Keras relies on its backend for low-level operations like convolutions and tensor products. While Keras supports several backend engines, its default backend is TensorFlow, with Google as its primary supporter.
In the next section, we will learn about the sequential model and the functional model of Keras.
