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 with TensorFlow on Google Cloud Platform
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Niveau avancé
Approx. 15 heures pour terminer
Anglais
Sous-titres : Anglais
Résultats de carrière des étudiants
50%
ont commencé une nouvelle carrière après avoir terminé ce cours
67%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
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
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau avancé
Approx. 15 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Google Cloud
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Programme du 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
14 vidéos
Course Introduction1 min
Getting Started with Google Cloud Platform and Qwiklabs3 min
Sequence data and models5 min
From sequences to inputs2 min
Modeling sequences with linear models2 min
Lab intro: using linear models for sequences20s
Lab solution: using linear models for sequences7 min
Modeling sequences with DNNs2 min
Lab intro: using DNNs for sequences19s
Lab solution: using DNNs for sequences2 min
Modeling sequences with CNNs3 min
Lab intro: using CNNs for sequences19s
Lab solution: using CNNs for sequences3 min
The variable-length problem4 min
1 lecture
How to send course feedback10 min
1 exercice pour s'entraîner
Working with Sequences
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 vidéos
How RNNs represent the past4 min
The limits of what RNNs can represent5 min
The vanishing gradient problem1 min
1 exercice pour s'entraîner
Recurrent Neural Networks
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)
14 vidéos
Introduction2 min
LSTMs and GRUs6 min
RNNs in TensorFlow2 min
Lab Intro: Time series prediction: end-to-end (rnn)45s
Lab Solution: Time series prediction: end-to-end (rnn)10 min
Deep RNNs1 min
Lab Intro: Time series prediction: end-to-end (rnn2)26s
Lab Solution: Time series prediction: end-to-end (rnn2)6 min
Improving our Loss Function2 min
Demo: Time series prediction: end-to-end (rnnN)3 min
Working with Real Data10 min
Lab Intro: Time Series Prediction - Temperature from Weather Data1 min
Lab Solution: Time Series Prediction - Temperature from Weather Data11 min
Summary1 min
1 exercice pour s'entraîner
Dealing with Longer Sequences
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)
8 vidéos
Working with Text1 min
Text Classification6 min
Selecting a Model2 min
Lab Intro: Text Classification47s
Lab Solution: Text Classification11 min
Python vs Native TensorFlow4 min
Demo: Text Classification with Native TensorFlow7 min
Summary1 min
1 exercice pour s'entraîner
Text Classification
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)
6 vidéos
Modern methods of making word embeddings8 min
Introducing TensorFlow Hub1 min
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub24s
Lab Solution: TensorFlow Hub9 min
Using TensorFlow Hub within an estimator1 min
1 exercice pour s'entraîner
Reusable Embeddings
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)
10 vidéos
Attention Networks4 min
Training Encoder-Decoder Models with TensorFlow6 min
Introducing Tensor2Tensor11 min
Lab Intro: Cloud poetry: Training custom text models on Cloud AI Platform1 min
Lab Solution: Cloud poetry: Training custom text models on Cloud AI Platform25 min
AutoML Translation4 min
Dialogflow6 min
Lab Intro: Introducing Dialogflow54s
Lab Solution: Dialogflow13 min
1 exercice pour s'entraîner
Encoder-Decoder Models
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
1 vidéo
Summary3 min
1 lecture
Additional Reading10 min
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 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
par HS22 juil. 2019
Though not focused on fundamental concepts, it's a great course to learn to use tensorflow and google cloud platform for sequence modelling.
À propos du Spécialisation Advanced Machine Learning with TensorFlow on Google Cloud Platform
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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