• The main steps for building a Neural Network are:

 – Define the model structure (such as number of input features and outputs)

 – Initialize the model’s weights and biases.

 – Loop.

       ∗ Calculate current loss (forward propagation)

       ∗ Calculate current gradient (backward propagation)

       ∗ Update parameters (gradient descent)

• Preprocessing the dataset is important.

• Tuning the learning rate (which is an example of a “hyperparameter”) can make a big difference to the algorithm.