TensorFlow Sequential Model with Dense Layers - Example and Explanation

This code defines a sequential model in TensorFlow. \n\nThe model consists of three dense layers. The first dense layer has 64 units and uses the ReLU activation function. It takes an input shape of (3,), which means it expects input data with three features. \n\nThe second dense layer also has 64 units and uses the ReLU activation function. \n\nThe last dense layer has 1 unit, which is the output of the model. There is no activation function specified for this layer, so it will default to a linear activation function.