This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text • Address time-series and text problems with recurrent neural networks • Choose between RNNs/LSTMs and simpler models • Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow COMPLETION CHALLENGE Complete any GCP specialization from November 5 - November 30, 2019 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details.
Este curso forma parte de Programa Especializado - Advanced Machine Learning with TensorFlow on Google Cloud Platform
Sequence Models for Time Series and Natural Language Processing
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Programa - Qué aprenderás en este curso
Working with Sequences
In this module, you’ll learn what a sequence is, see how you can prepare sequence data for modeling, and be introduced to some classical approaches to sequence modeling and practice applying them.
Recurrent Neural Networks
In this module, we introduce recurrent neural nets, explain how they address the variable-length sequence problem, explain how our traditional optimization procedure applies to RNNs, and review the limits of what RNNs can and can’t represent.
Dealing with Longer Sequences
In this module we dive deeper into RNNs. We’ll talk about LSTMs, Deep RNNs, working with real world data, and more.
Text Classification
In this module we look at different ways of working with text and how to create your own text classification models.
Reusable Embeddings
Labeled data for our classification models is expensive and precious. Here we will address how we can reuse pre-trained embeddings to make our models with TensorFlow Hub.
Encoder-Decoder Models
In this module, we focus on a sequence-to-sequence model called the encoder-decoder network to solve tasks, such as Machine Translation, Text Summarization and Question Answering.
Summary
In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data.
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Principales revisiones sobre Sequence Models for Time Series and Natural Language Processing
Great way to practically learn a lot of stuff. Sometimes, a lot of it starts to go over head. But, it is completely worth the learning curve! Definitely recommend it!
Excellent course for those who know RNN. Knowledge is refreshed and techniques are consolidated. More details about Google ecosystem is introduced.
Acerca de Google Cloud
Acerca de Programa especializado Advanced Machine Learning with TensorFlow on Google Cloud Platform
Preguntas Frecuentes
¿Puedo acceder a una vista preliminar del curso antes de inscribirme?
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¿Cuándo podré acceder a las lecciones y tareas?
Si decides inscribirte en el curso antes de la fecha de inicio de la sesión, tendrás acceso a todos los videos y las lecturas de la lección para el curso. Podrás enviar tareas en cuanto comience la sesión.
¿Qué recibiré cuando me inscriba?
Una vez que te inscribes y comienza la sesión, tendrás acceso a todos los videos y otros recursos, incluidos artículos de lectura y el foro de debate del curso. Podrás ver y enviar tareas de práctica y completar tareas con calificación obligatorias para obtener un título y un Certificado de curso
¿Cuándo recibiré mi Certificado de curso?
Si completas el curso de manera correcta, tu Certificado de curso electrónico se agregará a la página Logros. Desde allí, puedes imprimir tu Certificado de curso o agregarlo a tu perfil de LinkedIn
¿Por qué no puedo participar como oyente en este curso?
Este curso es uno de los pocos que se ofrecen en Coursera que está actualmente disponible solo para estudiantes que pagaron o que recibieron ayuda económica, si está disponible.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
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