Michał Jurzak

A simple workaround I found is just use the best hyperparameters in the build_model() function: ```Python md = build_model(**rnd_search_cv.best_params_) md.fit(X_train, y_train, epochs=100, validation_data=(X_valid, y_valid), callbacks=[keras.callbacks.EarlyStopping(patience=10)]) md.evaluate(X_test, y_test) ``` ``` 162/162...

I think the value of 0.5% is chosen arbitrarily just to increase from small learning rate (starting from 0.001). As was stated earlier in the chapter, the optimal learning rate...