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.
Este curso forma parte de Programa especializado: Advanced Machine Learning on Google Cloud
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Inglés (English)
Subtítulos: Inglés (English)
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 avanzado
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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
4 horas para completar
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.
4 horas para completar
14 videos (Total 41 minutos), 1 lectura, 4 cuestionarios
15 minutos para completar
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.
15 minutos para completar
4 videos (Total 15 minutos)
4 horas para completar
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.
4 horas para completar
14 videos (Total 62 minutos)
2 horas para completar
Text Classification
In this module we look at different ways of working with text and how to create your own text classification models.
2 horas para completar
8 videos (Total 35 minutos)
1 hora para completar
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.
1 hora para completar
6 videos (Total 28 minutos)
3 horas para completar
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.
3 horas para completar
10 videos (Total 84 minutos)
14 minutos para completar
Summary
In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data.
14 minutos para completar
1 video (Total 4 minutos), 1 lectura
Reseñas
Principales reseñas sobre SEQUENCE MODELS FOR TIME SERIES AND NATURAL LANGUAGE PROCESSING
por PR10 de ago. de 2019
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!
por AJ7 de jun. de 2021
The lack of synchronization between the videos and labs is extreme in this course, but the lectures are excellent and the subject area covered is very broad.
por ND28 de feb. de 2020
I have only one remark, please improve lesson regarding NLP by includin model build etc. Otherwise course is excellent and was helpful for me
por HS22 de jul. de 2019
Though not focused on fundamental concepts, it's a great course to learn to use tensorflow and google cloud platform for sequence modelling.
Acerca de Programa especializado: Advanced Machine Learning on Google Cloud
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.
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