Aprende Machine Learning Con Scikitlearn Keras Y Tensorflow

test_loss, test_acc = model.evaluate(x_test, y_test) print(f"Precisión en test: test_acc:.4f")

import tensorflow as tf from tensorflow import keras

model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.2) aprende machine learning con scikitlearn keras y tensorflow

: Aprende a limpiar datos, manejar valores faltantes y realizar escalado de características ( Feature Scaling ).

Para dominar estos temas en profundidad, te recomendamos el libro de referencia en la industria: (tercera edición) de Aurélien Géron. Resumen de Aprendizaje Usa Scikit-learn para tablas, regresiones y SVMs. test_loss, test_acc = model

dataset = tf.data.Dataset.from_tensor_slices((imagenes, etiquetas)) dataset = dataset.shuffle(1000).batch(32).prefetch(tf.data.AUTOTUNE)

Keras simplifica la construcción de redes neuronales gracias a su API secuencial o funcional. Aunque hoy es parte de TensorFlow ( tf.keras ), sigue siendo la forma más amigable de crear desde perceptrones multicapa hasta redes residuales. dataset = tf

Si intentas escribir una red neuronal directamente en TensorFlow "puro", podrías frustrarte con la complejidad. Keras simplifica esto. Con solo unas líneas, puedes definir la arquitectura de una red, elegir una función de pérdida y optimizar el entrenamiento. Conceptos clave a dominar:

Aprende Machine Learning con Scikit-Learn, Keras y TensorFlow: Guía Completa de Inicio a Fin

Es una API de alto nivel que funciona sobre TensorFlow. Keras hace que la construcción de redes neuronales sea simple, intuitiva y rápida. 2. Preparando el Entorno de Trabajo

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