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Entrenar y optimizar una red neuronal recurrente (RNN y LSTM)
Predecir series temporales con Facebook' Prophet
Predecir datos futuros con modelos de series temporales
En este proyecto aplicado y práctico aprenderás a entrenar redes neuronales recurrentes (RNN y LSTM) y modelos de Prophet para predecir series temporales. Tanto las redes LSTM como Prophet son algunos de los modelos más avanzados para predecir valores futuros en base a series de tiempo. Por ello, te enseñaremos a como pre-procesar y preparar tus datos, a entrenar los modelos, a evaluarlos, a optimizarlos y a utilizarlos para predecir datos futuros. Al finalizar este curso habrás aprendido a entrenar tus propios modelos y a aplicarlos en tus propios proyectos.
Deep Learning
Prophet
Time Series
Long Short-Term Memory (ISTM)
keras
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introducción a las series temporales
Fundamentos de Redes Neuronales Recurrentes (RNN y LSTM)
Funciones básicas con Keras
Pre-procesamiento de datos y entrenamiento del modelo LSTM
Ejercicio práctico. Desarrollo de un modelo LSTM
Evaluación del modelo y predicciones
Ejercicio práctico. Evaluación del modelo y predicción
Desarrollo de un modelo avanzado de LSTM
Ejercicio práctico. Modelo avanzado de LSTM
Predicción con nuevos datos y despliegue del modelo
Ejercicio práctico. Evaluación y puesta en producción de la red LSTM
Series temporales con Prophet
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
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