课程信息
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为12 小时

建议:3 semanas de estudio, de 5 a 7 horas por semana...
可选语言

西班牙语(Spanish)

字幕:西班牙语(Spanish), 法语(French), 巴西葡萄牙语, 德语(German), 英语(English), 日语...
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为12 小时

建议:3 semanas de estudio, de 5 a 7 horas por semana...
可选语言

西班牙语(Spanish)

字幕:西班牙语(Spanish), 法语(French), 巴西葡萄牙语, 德语(German), 英语(English), 日语...

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 3 分钟

Introducción

Descripción general del curso, en la que se destacan los módulos y objetivos clave. En primer lugar, analizaremos los aspectos del aprendizaje automático que requieren cierto grado de intuición, criterios adecuados y experimentación. Esto es lo que llamamos “el arte del AA”. También aprenderá los mecanismos necesarios para entrenar modelos. Los ajustará manualmente para observar los efectos en el rendimiento....
Reading
1 个视频 (总计 3 分钟)
Video1 个视频
完成时间(小时)
完成时间为 2 小时

El arte del AA

En este curso, aprenderá sobre el arte del aprendizaje automático. Analizaremos los aspectos del aprendizaje automático que requieren cierto grado de intuición, criterio y experimentación para encontrar el balance adecuado y obtener resultados satisfactorios (sepa de antemano que es imposible lograr la perfección)....
Reading
10 个视频 (总计 29 分钟), 3 个测验
Video10 个视频
Regularización4分钟
Regularizaciones L1 y L24分钟
Introducción al lab Regularización12s
Lab: Regularización2分钟
Tasa de aprendizaje y tamaño de lotes5分钟
Optimización1分钟
Práctica con el código de Tensorflow1分钟
Introducción al lab Ajuste manual de modelos de AA18s
Solución del lab Ajuste manual de modelos de AA7分钟
Quiz2 个练习
El arte del AA2分钟
Tasa de aprendizaje y tamaño del lote10分钟
完成时间(小时)
完成时间为 1 小时

Ajuste de hiperparámetros

En este módulo, aprenderá a diferenciar los parámetros de los hiperparámetros. Más adelante, analizaremos el enfoque de búsqueda por cuadrículas tradicional y aprenderemos a pensar más allá mediante la implementación de algoritmos más inteligentes. Por último, aprenderá cómo Cloud ML Engine hace que el ajuste automático de los hiperparámetros sea tan conveniente....
Reading
5 个视频 (总计 8 分钟), 2 个测验
Video5 个视频
Parámetros frente a hiperparámetros2分钟
Más allá de la búsqueda por cuadrícula3分钟
Introducción al lab Mejore la exactitud de un modelo mediante el ajuste de hiperparámetros con Cloud MLE23s
Solución del lab Mejore la exactitud de un modelo mediante el ajuste de hiperparámetros con Cloud MLE30s
Quiz1 个练习
Ajuste de hiperparámetros8分钟
2
完成时间(小时)
完成时间为 1 小时

Un poco de ciencia

En este módulo, agregaremos un poco de ciencia al arte del aprendizaje automático. En primer lugar, abordaremos cómo regularizar los modelos para lograr dispersión, a fin de que sean más simples y concisos. Más adelante, analizaremos la regresión logística y aprenderemos a evaluar el rendimiento....
Reading
5 个视频 (总计 28 分钟), 2 个测验
Video5 个视频
Regularización para lograr dispersión5分钟
Lab: Regularización L13分钟
Solución del lab Regularización L151s
Regresión logística17分钟
Quiz2 个练习
Regularización L14分钟
Regresión logística2分钟
完成时间(小时)
完成时间为 2 小时

La ciencia de las redes neuronales

En este módulo, profundizaremos en los aspectos científicos del aprendizaje automático, específicamente en las redes neuronales....
Reading
6 个视频 (总计 70 分钟), 3 个测验
Video6 个视频
Redes neuronales18分钟
Lab: Sitio de prueba de redes neuronales12分钟
Entrenamiento de redes neuronales14分钟
Lab: Uso de redes neuronales para compilar modelos de AA11分钟
Redes neuronales de varias clases10分钟
Quiz2 个练习
Cómo entrenar redes neuronales8分钟
Redes neuronales de clases múltiples4分钟
3
完成时间(小时)
完成时间为 1 小时

Incorporaciones

En este módulo, aprenderá a utilizar incorporaciones para administrar datos dispersos, de modo que los modelos de aprendizaje automático que usan este tipo de datos consuman menos memoria y puedan entrenarse más rápido. Las incorporaciones también son una forma de reducir la dimensionalidad y, de esta manera, simplificar los modelos para que sean más generalizables....
Reading
7 个视频 (总计 31 分钟), 1 个测验
Video7 个视频
Repaso de las incorporaciones5分钟
Recomendaciones4分钟
Incorporaciones basadas en datos3分钟
Tensores dispersos4分钟
Entrenamiento de una incorporación4分钟
Propiedad de similitud7分钟
Quiz1 个练习
Incorporaciones4分钟
完成时间(小时)
完成时间为 2 小时

Estimador personalizado

En este módulo, iremos más allá del uso de estimadores prediseñados y escribiremos un estimador personalizado. Al escribir un estimador personalizado, obtendrá un mayor control de la función del modelo....
Reading
5 个视频 (总计 30 分钟), 2 个测验
Video5 个视频
Función de modelos6分钟
Lab: Implementación de un estimador personalizado11分钟
Modelos de Keras4分钟
Demostración de modelos de Keras y del estimador2分钟
Quiz1 个练习
Estimador personalizado6分钟
完成时间(小时)
完成时间为 5 分钟

Resumen

Repaso de los conceptos clave abordados en el curso Art and Science of ML....
Reading
2 个视频 (总计 5 分钟)
Video2 个视频
Resumen1分钟
Resumen de la especialización2分钟

关于 Google Cloud

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success....

关于 Machine Learning with TensorFlow on Google Cloud Platform en Español 专项课程

>>>Si se inscribe en esta especialización acepta las Condiciones del Servicio de Qwiklabs, que se establecen en las Preguntas Frecuentes. Puede consultarlas en https://qwiklabs.com/terms_of_service.<<< ***Google Cloud y Kaggle desean invitarlo a participar en nuestra competencia amistosa de predicción de tarifas de taxis de Nueva York, que se está realizando en este momento. Para obtener más información, visite: https://www.kaggle.com/c/new-york-city-taxi-fare-prediction.*** ¿Qué es el aprendizaje automático? ¿Qué tipos de problemas puede solucionar? ¿Cuáles son las cinco fases para convertir un posible caso práctico en un recurso que pueda aprovechar la tecnología de aprendizaje automático? ¿Por qué es importante no saltarse fases? ¿Por qué las redes neuronales son tan populares? ¿Cómo podría plantear un problema de aprendizaje supervisado y encontrar una buena solución generalizable mediante un descenso de gradientes y una forma meditada de crear conjuntos de datos? Aprenda a escribir modelos de aprendizaje automático distribuido que escalen en Tensorflow, que escalen de forma horizontal en el entrenamiento de esos modelos y que brinden predicciones de alto rendimiento. Convierta los datos sin procesar en funciones de una forma que permita al AA aprender características importantes de los datos y aportar una percepción humana para abordar los problemas. Por último, aprenda a incorporar la combinación adecuada de parámetros que desarrolle modelos generalizados y exactos, y conozca la teoría para solucionar determinados tipos de problemas de AA. Experimentará con el AA de extremo a extremo, a partir de la construcción de una estrategia centrada en el AA y el avance hacia el entrenamiento, optimización y producción de modelos con labs prácticos mediante Google Cloud Platform....
Machine Learning with TensorFlow on Google Cloud Platform en Español

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    General

    This Agreement shall be governed by Massachusetts law and controlling United States federal law, without regard to the choice or conflicts of law provisions of any jurisdiction, and any disputes, actions, claims, or causes of action arising out of or in connection with this Agreement or the Service shall be subject to the exclusive jurisdiction of the state and federal courts located in Massachusetts. If any provision of this Agreement is held by a court of competent jurisdiction to be invalid or unenforceable, then such provision(s) shall be construed, as nearly as possible, to reflect the intentions of the invalid or unenforceable provision(s), with all other provisions remaining in full force and effect. All notices to be delivered under this Agreement shall be sent via postal mail to the applicable party at the address notified by the applicable party in writing. Notice shall be deemed to have been given forty-eight (48) hours after the mailing of the written notice. No joint venture, partnership, employment, or agency relationship exists between you and Cloud vLab as a result of this agreement or use of the Service. The failure of either party to enforce any right or provision in this Agreement shall not constitute a waiver of such right or provision unless acknowledged and agreed to by the waiving party in writing. The section headings herein are included merely for convenience of reference, do not limit or affect any of the contents of this Agreement, and are not to be considered part of, or to be used in interpreting, this Agreement. Cloud vLab may assign this Agreement at any time without prior notice to you. You may not assign this Agreement without the prior written consent of Cloud vLab, which consent may be withheld by Cloud vLab at its sole discretion. Any purported assignment by you in violation of this section shall be void. This Agreement, which incorporates the Cloud vLab Privacy Policy comprises the entire agreement between you and Cloud vLab and governs your use of the Service, superseding all prior or contemporaneous negotiations, discussions or agreements, whether written or oral, between the Parties regarding the subject matter hereof.

    Last Updated: September 1, 2015

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