This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.
ofrecido por
Google CloudAcerca de este Curso
44.770 vistas recientes
Fechas límite flexibles
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Certificado para compartir
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100 % en línea
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Nivel intermedio
Aprox. 19 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
Use the Keras Sequential and Functional APIs for simple and advanced model creation
Design and build a TensorFlow 2.x input data pipeline
Use the tf.data library to manipulate data and large datasets
Train, deploy, and productionalize ML models at scale with Cloud AI Platform
Habilidades que obtendrás
Machine LearningPython ProgrammingBuild Input Data PipelineTensorflowkeras
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 19 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
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.
Programa - Qué aprenderás en este curso
7 minutos para completar
Introduction to course
This course is an introduction to TensorFlow 2.x, which incorporates the ease of use of Keras for building machine learning models. This course covers designing and building a TensorFlow 2.x input data pipeline, building machine learning models with TensorFlow 2.x and Keras, improving the accuracy of machine learning models, and writing machine learning models for scaled use.
7 minutos para completar
2 videos (Total 7 minutos)
3 horas para completar
Introduction to TensorFlow
We will introduce you to the new paradigm of TensorFlow 2.x. You will learn about the the TensorFlow API hierarchy and will get to know the main components of TensorFlow, tensors and variables, through hands-on exercises.
3 horas para completar
5 videos (Total 22 minutos), 1 lectura, 5 cuestionarios
8 horas para completar
Design and Build a TensorFlow Input Data Pipeline
We will introduce you to working with datasets and feature columns. You will get hands-on practice loading csv data, numPy arrays with tf.data.Dataset, text data, and load images using tf.data.Dataset. You will also get hands on practice creating numeric, categorical, bucketized, and hashed feature columns.
8 horas para completar
10 videos (Total 25 minutos), 1 lectura, 9 cuestionarios
4 horas para completar
Training neural networks with Tensorflow 2 and the Keras Sequential API
In this module you will get introduced to writing TensorFlow models using the Keras Sequential API. But, before you jump right into writing the model, we will talk about activation functions, loss and optimization. You will then be introduced to the Keras Sequential API to learn how you can create deep learning models with it. You will also learn how to deploy the model for prediction in the cloud.
4 horas para completar
5 videos (Total 23 minutos), 1 lectura, 5 cuestionarios
3 horas para completar
Training neural networks with Tensorflow 2 and the Keras Functional API
The Sequential model API is great for developing deep learning models in most situations, but it also has some limitations. One example is that it is not straightforward to define models that may have multiple different input sources, produce multiple output destinations or models that re-use layers. The Keras Functional API is a way to create models that are more flexible than the tf.keras.Sequential API. The functional API can handle models with non-linear topology, models with shared layers, and models with multiple inputs or outputs. The Keras Functional API provides a more flexible way for defining models. It specifically allows you to define multiple input or output models as well as models that share layers. More than that, it allows you to define ad hoc acyclic network graphs. The main idea that a deep learning model is usually a directed acyclic graph (DAG) of layers. So the Functional API is a way to build graphs of layers. Additionally, we will also talk about how regularization can help with model performance.
3 horas para completar
6 videos (Total 29 minutos), 1 lectura, 4 cuestionarios
1 hora para completar
Summary
Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the tf.data API, Keras Sequential and Functional APIs and end with scaling your machine learning models with Cloud AI Platform.
1 hora para completar
1 video (Total 8 minutos), 2 lecturas, 1 cuestionario
Reseñas
Principales reseñas sobre INTRODUCCIÓN A TENSORFLOW
por AJ14 de nov. de 2020
Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy'). Evan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.
por VC17 de may. de 2020
I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.
por LL30 de ago. de 2019
The tools and methods presented were great. The instructors were also fantastic. However the coding exercises were lacking in guidance even though the complete solution is given in the video.
por DW16 de oct. de 2018
pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!
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