… with many neurons. Ideally, we can imagine each layer as having a particular purpose; for example, the second layer (first hidden layer) will recognize the outline of the animal, the third layer will recognize certain shapes (such as circles), the fourth layer will recognize animal parts (for example, a circle within a circle may be an eye and a pupil), and the last (output) layer will recognize whether it is a cat or dog based on the characteristics of the animal parts.
…the number of iterations for an algorithm such as gbm and random forest is called “Early Stopping”. Early Stopping performs model optimisation by monitoring the model’s performance on a separate test data set and stopping the training procedure once the performance on the test data stops improving beyond a certain number of iterations.