Google Cloud
Art and Science of Machine Learning en Español

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Google Cloud

Art and Science of Machine Learning en Español

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Gain insight into a topic and learn the fundamentals.
4.7

(51 reviews)

Intermediate level
Some related experience required
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.7

(51 reviews)

Intermediate level
Some related experience required
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Generalizar un modelo de AA usando técnicas de regularización

  • Ajustar el tamaño del lote y la tasa de aprendizaje para mejorar el rendimiento del modelo

  • Optimizar el modelo

  • Aplicar los conceptos en el código de TensorFlow

Details to know

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Assessments

7 assignments

Taught in Spanish

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This course is part of the Machine Learning with TensorFlow on Google Cloud en Español Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 7 modules in this course

Le damos la bienvenida a The Art and Science of Machine Learning. En este curso, se abordan las habilidades básicas de intuición, buen criterio y experimentación del AA necesarias para ajustar mejor y optimizar modelos de AA a fin de lograr el mejor rendimiento. Aprenderá a generalizar su modelo usando técnicas de regularización y descubrirá los efectos de los hiperparámetros, como el tamaño del lote y la tasa de aprendizaje, en el rendimiento del modelo. Analizaremos algunos de los algoritmos de optimización de los modelos más comunes y le mostraremos cómo especificar un método de optimización en su código de TensorFlow.

What's included

1 video

En este módulo, aprenderá a ajustar el tamaño del lote y la tasa de aprendizaje para mejorar el rendimiento del modelo, optimizar su modelo y aplicar los conceptos en el código de TensorFlow.

What's included

8 videos2 readings1 assignment1 app item

En este módulo, aprenderá a diferenciar entre parámetros e hiperparámetros. Luego, veremos el enfoque tradicional de búsqueda por cuadrícula y aprenderemos a ir más allá mediante algoritmos más inteligentes. Por último, verá cómo Cloud ML Engine facilita la automatización del ajuste de hiperparámetros.

What's included

5 videos1 reading1 assignment2 app items

En este módulo, comenzaremos a presentar la ciencia junto con el arte del aprendizaje automático. Primero, hablaremos sobre cómo realizar una regularización para lograr dispersión y, de este modo, conseguir modelos más simples y concisos. Luego, abordaremos la regresión logística y aprenderemos a determinar el rendimiento.

What's included

5 videos2 readings2 assignments

En este módulo, profundizaremos en la ciencia, específicamente en las redes neuronales.

What's included

7 videos1 reading2 assignments2 app items

En este módulo, aprenderá a usar incorporaciones para administrar los datos dispersos, a fin de que los modelos de aprendizaje automático que usan ese tipo de datos consuman menos memoria y se entrenen más rápido. Las incorporaciones también son una forma de reducir la dimensionalidad. Esto hace que los modelos sean más simples y generalizables.

What's included

8 videos1 reading1 assignment1 app item

Resumen de los puntos clave de aprendizaje del curso.

What's included

1 video3 readings

Instructor

Instructor ratings
4.5 (16 ratings)
Google Cloud Training
Google Cloud
1,668 Courses2,752,615 learners

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Google Cloud

Recommended if you're interested in Machine Learning

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4.7

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