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.
Ce cours fait partie de la Spécialisation Advanced Machine Learning on Google Cloud
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Cours 4 sur 5 dans le
Niveau avancé
Approx. 15 heures pour terminer
Anglais
Sous-titres : Anglais
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 5 dans le
Niveau avancé
Approx. 15 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme de cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
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 heures pour terminer
14 vidéos (Total 41 min), 1 lecture, 4 quiz
15 minutes pour terminer
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 minutes pour terminer
4 vidéos (Total 15 min)
4 heures pour terminer
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 heures pour terminer
14 vidéos (Total 62 min)
2 heures pour terminer
Text Classification
In this module we look at different ways of working with text and how to create your own text classification models.
2 heures pour terminer
8 vidéos (Total 35 min)
1 heure pour terminer
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 heure pour terminer
6 vidéos (Total 28 min)
3 heures pour terminer
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 heures pour terminer
10 vidéos (Total 84 min)
14 minutes pour terminer
Summary
In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data.
14 minutes pour terminer
1 vidéo (Total 4 min), 1 lecture
Avis
Meilleurs avis pour SEQUENCE MODELS FOR TIME SERIES AND NATURAL LANGUAGE PROCESSING
par PR10 août 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!
par AJ7 juin 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.
par JW10 nov. 2018
Excellent course for those who know RNN. Knowledge is refreshed and techniques are consolidated. More details about Google ecosystem is introduced.
par ND28 févr. 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
À propos du Spécialisation 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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