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.
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第 4 門課程（共 5 門）
高級
完成時間大約為15 小時
英語（English）
字幕：英語（English）
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 5 門）
高級
完成時間大約為15 小時
英語（English）
字幕：英語（English）
提供方
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.
授課大綱 - 您將從這門課程中學到什麼
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.
完成時間為 4 小時
14 個視頻 （總計 41 分鐘）, 1 個閱讀材料, 4 個測驗
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.
完成時間為 15 分鐘
4 個視頻 （總計 15 分鐘）
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.
完成時間為 4 小時
14 個視頻 （總計 62 分鐘）
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.
完成時間為 2 小時
8 個視頻 （總計 35 分鐘）
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.
完成時間為 1 小時
6 個視頻 （總計 28 分鐘）
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.
完成時間為 3 小時
10 個視頻 （總計 84 分鐘）
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.
完成時間為 14 分鐘
1 個視頻 （總計 4 分鐘）, 1 個閱讀材料
審閱
- 5 stars63.50%
- 4 stars22.36%
- 3 stars8.22%
- 2 stars2.95%
- 1 star2.95%
來自SEQUENCE MODELS FOR TIME SERIES AND NATURAL LANGUAGE PROCESSING的熱門評論
由 AS 提供2019年8月16日
I like it because it is very relevant to my work. The dialogflow part is a bit weak. I am not sure if it is the product or the course.
由 AF 提供2019年5月31日
Lot's of good information. I cannot wait to start using this knowledge. Thank you!
由 ND 提供2020年2月28日
I have only one remark, please improve lesson regarding NLP by includin model build etc. Otherwise course is excellent and was helpful for me
由 MC 提供2019年12月15日
Awesome course, Great tutors they teach tough topics very easily like RNN(LSTM, GRU), Encode Decoder and attention.
關於 Advanced Machine Learning on Google Cloud 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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