scikit‑learn:
def crear_modelo(optimizer="adam", neurons=64): model = keras.Sequential([ layers.Dense(neurons, activation="relu", input_shape=(X_train.shape[1],)), layers.Dense(1, activation="sigmoid") ]) model.compile(optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"]) return model aprende machine learning con scikitlearn keras y tensorflow
This paper explores the distinct paradigms of Classical Machine Learning and Deep Learning as presented in Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow . It contrasts the statistical approaches implemented in Scikit-Learn with the representation learning capabilities of Keras and TensorFlow. By analyzing the data preprocessing requirements, model complexity, and optimization strategies of both frameworks, this paper establishes a guideline for selecting the appropriate toolset for specific data science problems, ranging from structured tabular data to unstructured perceptual data. scikit‑learn: def crear_modelo(optimizer="adam"