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
本課程是 Advanced Machine Learning with TensorFlow on Google Cloud Platform 專項課程 專項課程的一部分
Sequence Models for Time Series and Natural Language Processing
提供方
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 4 小時
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.
14 個視頻 （總計 41 分鐘）, 1 個閱讀材料, 4 個測驗
14 個視頻
Getting Started with Google Cloud Platform and Qwiklabs3分鐘
Sequence data and models5分鐘
From sequences to inputs2分鐘
Modeling sequences with linear models2分鐘
Lab intro: using linear models for sequences20
Lab solution: using linear models for sequences7分鐘
Modeling sequences with DNNs2分鐘
Lab intro: using DNNs for sequences19
Lab solution: using DNNs for sequences2分鐘
Modeling sequences with CNNs3分鐘
Lab intro: using CNNs for sequences19
Lab solution: using CNNs for sequences3分鐘
The variable-length problem4分鐘
1 個閱讀材料
How to send course feedback10分鐘
1 個練習
Working with Sequences
完成時間為 15 分鐘
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.
4 個視頻 （總計 15 分鐘）, 1 個測驗
4 個視頻
How RNNs represent the past4分鐘
The limits of what RNNs can represent5分鐘
The vanishing gradient problem1分鐘
1 個練習
Recurrent Neural Networks
完成時間為 4 小時
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.
14 個視頻 （總計 62 分鐘）, 4 個測驗
14 個視頻
Introduction2分鐘
LSTMs and GRUs6分鐘
RNNs in TensorFlow2分鐘
Lab Intro: Time series prediction: end-to-end (rnn)45
Lab Solution: Time series prediction: end-to-end (rnn)10分鐘
Deep RNNs1分鐘
Lab Intro: Time series prediction: end-to-end (rnn2)26
Lab Solution: Time series prediction: end-to-end (rnn2)6分鐘
Improving our Loss Function2分鐘
Demo: Time series prediction: end-to-end (rnnN)3分鐘
Working with Real Data10分鐘
Lab Intro: Time Series Prediction - Temperature from Weather Data1分鐘
Lab Solution: Time Series Prediction - Temperature from Weather Data11分鐘
Summary1分鐘
1 個練習
Dealing with Longer Sequences
週
2完成時間為 2 小時
Text Classification
In this module we look at different ways of working with text and how to create your own text classification models.
8 個視頻 （總計 35 分鐘）, 2 個測驗
8 個視頻
Text Classification6分鐘
Selecting a Model2分鐘
Lab Intro: Text Classification47
Lab Solution: Text Classification11分鐘
Python vs Native TensorFlow4分鐘
Demo: Text Classification with Native TensorFlow7分鐘
Summary1分鐘
1 個練習
Text Classification
完成時間為 1 小時
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.
6 個視頻 （總計 28 分鐘）, 2 個測驗
6 個視頻
Modern methods of making word embeddings8分鐘
Introducing TensorFlow Hub1分鐘
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub24
Lab Solution: TensorFlow Hub9分鐘
Using TensorFlow Hub within an estimator1分鐘
1 個練習
Reusable Embeddings
完成時間為 3 小時
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.
10 個視頻 （總計 84 分鐘）, 3 個測驗
10 個視頻
Attention Networks4分鐘
Training Encoder-Decoder Models with TensorFlow6分鐘
Introducing Tensor2Tensor11分鐘
Lab Intro: Cloud poetry: Training custom text models on Cloud AI Platform1分鐘
Lab Solution: Cloud poetry: Training custom text models on Cloud AI Platform25分鐘
AutoML Translation4分鐘
Dialogflow6分鐘
Lab Intro: Introducing Dialogflow54
Lab Solution: Dialogflow13分鐘
1 個練習
Encoder-Decoder Models
完成時間為 14 分鐘
Summary
In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data.
1 個視頻 （總計 4 分鐘）, 1 個閱讀材料
1 個視頻
Summary3分鐘
1 個閱讀材料
Additional Reading10分鐘
4.4
24 個審閱50%
完成這些課程後已開始新的職業生涯
50%
通過此課程獲得實實在在的工作福利
來自Sequence Models for Time Series and Natural Language Processing的熱門評論
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.
關於 Google 云端平台
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.
關於 Advanced Machine Learning with TensorFlow on Google Cloud Platform 專項課程
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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