mglearn plots plot_two_hidden_layer_graph(figure - multilayer perceptron with two hidden layers having large neural networks made up of many of these layers of computation is what inspired the term "deep learning tuning neural networks let' look into the workings of the mlp by applying the mlpclassifier to the two_moons dataset we used earlier in this the results are shown in figure - in[ ]from sklearn neural_network import mlpclassifier from sklearn datasets import make_moons xy make_moons(n_samples= noise= random_state= x_trainx_testy_trainy_test train_test_split(xystratify=yrandom_state= mlp mlpclassifier(algorithm=' -bfgs'random_state= fit(x_trainy_trainmglearn plots plot_ d_separator(mlpx_trainfill=truealpha mglearn discrete_scatter(x_train[: ]x_train[: ]y_trainplt xlabel("feature "plt ylabel("feature " supervised learning