Intro to TensorFlow en Español from Coursera

Este curso se enfoca en aprovechar la flexibilidad y facilidad de uso de TensorFlow 2.x y Keras para compilar, entrenar e implementar modelos de aprendizaje automático. Aprenderá sobre la jerarquía de la API de TensorFlow 2.x y conocerá los componentes principales de TensorFlow mediante ejercicios prácticos. Le mostraremos cómo trabajar con conjuntos de datos y columnas de atributos. Aprenderá a diseñar y compilar una canalización de datos de entrada de TensorFlow 2.x. Adquirirá experiencia práctica en la carga de arreglos de NumPy, imágenes y datos de texto con tf.data.Dataset, así como de datos de CSV con Pandas. También adquirirá experiencia práctica en la creación de columnas de atributos numéricas, categóricas, agrupadas en depósitos y con hash.

Además, le presentaremos las API secuencial y funcional de Keras para mostrarle cómo crear modelos de aprendizaje profundo. Hablaremos sobre las funciones de activación, pérdida y optimización. Nuestros labs prácticos sobre los notebooks de Jupyter le permitirán compilar modelos de aprendizaje automático de regresión lineal básica, y de regresión logística básica y avanzada. Aprenderá a entrenar, implementar y llevar a producción modelos de aprendizaje automático a gran escala con AI Platform de Cloud.

What's inside

Syllabus

Introducción al curso

Este curso es una introducción a TensorFlow 2.x, que incorpora la facilidad de uso de Keras para compilar modelos de aprendizaje automático. En este curso, se abarcarán el diseño y la compilación de una canalización de datos de entrada de TensorFlow 2.x., la compilación de modelos de aprendizaje automático con TensorFlow 2.x y Keras, la mejora en la exactitud de estos modelos y su correspondiente escritura para una utilización escalada.

Introducción a TensorFlow

Le mostraremos el nuevo paradigma de TensorFlow 2.x. Aprenderá sobre la jerarquía de la API de TensorFlow y conocerá los componentes principales de TensorFlow, los tensores y las variables, mediante ejercicios prácticos.

Diseñe y compile una canalización de datos de entrada de TensorFlow

Le mostraremos cómo trabajar con conjuntos de datos y columnas de atributos. Adquirirá experiencia práctica en la carga de arreglos de NumPy, imágenes y datos de texto con tf.data.Dataset, así como de datos de CSV con Pandas. También adquirirá experiencia práctica en la creación de columnas de atributos numéricas, categóricas, agrupadas en depósitos y con hash.

Entrenamiento de redes neuronales con TensorFlow 2 y la API secuencial de Keras

En este módulo, le mostraremos cómo escribir modelos de TensorFlow con la API secuencial de Keras, pero, antes de sumergirnos en la escritura del modelo, hablaremos sobre las funciones de activación, pérdida y optimización. Luego, se le presentará la API secuencial de Keras para mostrarle cómo crear modelos de aprendizaje profundo. Además, aprenderá a implementar el modelo para la predicción en la nube.

Entrenamiento de redes neuronales con TensorFlow 2 y la API funcional de Keras

En la mayoría de las situaciones, la API del modelo Sequential es ideal para desarrollar modelos de aprendizaje profundo, pero tiene algunas limitaciones. Por ejemplo, no define de forma directa los modelos que pueden tener varias fuentes de entrada o producir varios destinos de salida, como tampoco los modelos que reutilizan capas. La API funcional de Keras es una forma de crear modelos más flexibles que los de la API de tf.keras.Sequential. La API funcional procesa modelos con topología no lineal, capas compartidas, y varias salidas y entradas. La API funcional de Keras proporciona una forma más flexible de definir los modelos. Específicamente, le permite definir varios modelos de entrada y salida, así como los que comparten capas. Además, le permite definir grafos acíclicos de red ad hoc. La principal idea es que un modelo de aprendizaje profundo suele ser un grafo acíclico dirigido (DAG) de capas. Por lo tanto, la API funcional es una forma de compilar grafos de capas. En este módulo, también hablaremos sobre cómo la regularización puede ayudar con el rendimiento del modelo.

Resumen

Haremos un resumen de los temas sobre TensorFlow que tratamos hasta aquí en este curso. Repasaremos el código principal de TensorFlow, la API de tf.data, las API secuencial y funcional de Keras, y finalizaremos con el escalamiento de sus modelos de aprendizaje automático mediante AI Platform de Cloud.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores TensorFlow 2.x and Keras, industry-standard tools for building and deploying machine learning models

Taught by Google Cloud Training, recognized for their work in machine learning and AI

Develops skills in designing and implementing TensorFlow data pipelines, building machine learning models with TensorFlow and Keras, and improving model accuracy

Provides hands-on labs and interactive materials for practical experience

Suitable for learners with prior programming experience and a basic understanding of machine learning concepts

Emphasizes deploying and scaling machine learning models using Cloud AI Platform, which may require additional costs

Reviews summary

Tensorflow in spanish: practical

This Intro to TensorFlow course in Spanish assists learners in getting started with TensorFlow 2.x and Keras for developing machine learning models. Focused on data pipeline design, neural network training, and model optimization techniques, the course provides a solid foundation in TensorFlow.

Covers regularization techniques

"En este módulo, también hablaremos sobre cómo la regularización puede ayudar con el rendimiento del modelo."

AI Platform for model deployment

"Aprenderá a entrenar, implementar y llevar a producción modelos de aprendizaje automático a gran escala con AI Platform de Cloud."

Covers TensorFlow 2.x

"Este curso se enfoca en aprovechar ... TensorFlow 2.x ..."

Introduces basic TensorFlow concepts

"Aprenderá sobre la jerarquía de la API de TensorFlow 2.x y conocerá los componentes principales de TensorFlow mediante ejercicios prácticos."

Lots of hands-on exercises

"Aprendí sobre ... mediante ejercicios prácticos."

Some outdated content

"des actualizado , ya se trabaja mucho mas sencillo con tensorflow 2.x y en el curso aun con la versión 1.x"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Intro to TensorFlow en Español with these activities:

Keras Model Customization Tutorials

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Deepen your understanding of Keras model customization by following guided tutorials that cover advanced techniques.

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Follow along with tutorials on how to create and train custom Keras models using the sequential and functional API.
Learn how to use Keras layers, optimizers, loss functions, and metrics to fine-tune your models.
Explore techniques for regularizing and evaluating Keras models to improve their performance and generalization capabilities.

TensorFlow Classification Project

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Apply your TensorFlow skills to a practical project by building a classification model to solve a real-world problem.

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Define the problem you want to solve and gather the necessary data.
Design and implement a TensorFlow model for the classification task.
Train and evaluate your model on the dataset.
Deploy your model to a platform or application for real-world use.

TensorFlow Best Practices Blog Post

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Enhance your understanding and help others by sharing your knowledge and insights on TensorFlow best practices in a blog post.

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Identify common challenges and pitfalls in TensorFlow development.
Research and gather information on best practices and techniques.
Create a well-written and informative blog post summarizing your findings.

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Career center

Learners who complete Intro to TensorFlow en Español will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist collects, analyzes, and interprets data to help organizations make better decisions. TensorFlow is a powerful tool that can be used to analyze and interpret data. By taking this course, you will learn how to use TensorFlow to analyze data and solve real-world problems.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and maintains machine learning models and algorithms. TensorFlow is an open-source machine learning library that makes it easy for Machine Learning Engineers to build and train machine learning models. By taking this course, you will acquire the knowledge and skills to use TensorFlow to build and train your own machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software applications. TensorFlow is a popular machine learning library that can be used to develop and maintain machine learning applications. By taking this course, you will learn how to use TensorFlow to develop and maintain your own machine learning applications.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst collects, analyzes, and interprets data to help organizations make better decisions. TensorFlow is a powerful tool that can be used to analyze and interpret data. By taking this course, you will learn how to use TensorFlow to analyze data and solve real-world problems.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

A Quantitative Analyst develops and uses mathematical and statistical models to analyze and forecast financial data. TensorFlow is a powerful tool that can be used to develop and use mathematical and statistical models. By taking this course, you will learn how to use TensorFlow to develop and use your own financial models.

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Operations Research Analyst

An Operations Research Analyst develops and uses mathematical and statistical models to solve complex business problems. TensorFlow is a powerful tool that can be used to develop and use mathematical and statistical models. By taking this course, you will learn how to use TensorFlow to develop and use your own models to solve complex business problems.

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Business Analyst

A Business Analyst analyzes and interprets business data to help organizations make better decisions. TensorFlow is a powerful tool that can be used to analyze and interpret business data. By taking this course, you will learn how to use TensorFlow to analyze business data and solve real-world problems.

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Product Manager

A Product Manager develops and manages products and services. TensorFlow is a powerful tool that can be used to develop and manage machine learning products and services. By taking this course, you will learn how to use TensorFlow to develop and manage your own machine learning products and services.

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Marketing Manager

A Marketing Manager develops and manages marketing campaigns and strategies. TensorFlow is a powerful tool that can be used to develop and manage machine learning marketing campaigns and strategies. By taking this course, you will learn how to use TensorFlow to develop and manage your own machine learning marketing campaigns and strategies.

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Sales Manager

A Sales Manager develops and manages sales teams and strategies. TensorFlow is a powerful tool that can be used to develop and manage machine learning sales teams and strategies. By taking this course, you will learn how to use TensorFlow to develop and manage your own machine learning sales teams and strategies.

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Technical Writer

A Technical Writer writes and edits technical documentation. TensorFlow is a powerful tool that can be used to write and edit technical documentation. By taking this course, you will learn how to use TensorFlow to write and edit your own technical documentation.

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Customer Success Manager

A Customer Success Manager helps customers succeed with a company's products and services. TensorFlow is a powerful tool that can be used to help customers succeed with a company's machine learning products and services. By taking this course, you will learn how to use TensorFlow to help customers succeed with your company's machine learning products and services.

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Teacher

A Teacher teaches students about a particular subject. TensorFlow is a powerful tool that can be used to teach students about machine learning. By taking this course, you will learn how to use TensorFlow to teach your own students about machine learning.

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Librarian

A Librarian helps people find and access information. TensorFlow is a powerful tool that can be used to help people find and access information about machine learning. By taking this course, you will learn how to use TensorFlow to help people find and access information about machine learning.

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Archivist

An Archivist preserves and manages historical records. TensorFlow is a powerful tool that can be used to preserve and manage historical records about machine learning. By taking this course, you will learn how to use TensorFlow to preserve and manage your own historical records about machine learning.

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