關於此課程：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
此課程適用人群：Data Engineers and programmers interested in learning how to apply machine learning in practice. Anyone interested in learning how to leverage machine learning in their enterprise.
製作方： Google Cloud
| 基本信息 | |
| 級別 | 高級設置 |
| 語言 | English |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
預覽
授課大綱
第 1 週
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, 1 個閱讀材料
- 視頻: Getting Started with Google Cloud Platform and Qwiklabs
- Reading: How to send course feedback
- 視頻: Sequence data and models
- 視頻: From sequences to inputs
- 視頻: Modeling sequences with linear models
- 視頻: Lab intro: using linear models for sequences
- 視頻: Lab solution: using linear models for sequences
- 視頻: Modeling sequences with DNNs
- 視頻: Lab intro: using DNNs for sequences
- 視頻: Lab solution: using DNNs for sequences
- 視頻: Modeling sequences with CNNs
- 視頻: Lab intro: using CNNs for sequences
- 視頻: Lab solution: using CNNs for sequences
- 視頻: The variable-length problem
已評分: Qwiklabs – Using linear models for sequences
已評分: Qwiklabs – Using DNNs for sequences
已評分: Qwiklabs – Using CNNs for sequences
已評分: Working with Sequences
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
- 視頻: How RNNs represent the past
- 視頻: The limits of what RNNs can represent
- 視頻: The vanishing gradient problem
已評分: Recurrent Neural Networks
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
- 視頻: LSTMs and GRUs
- 視頻: RNNs in TensorFlow
- 視頻: Lab Intro: Time series prediction: end-to-end (rnn)
- 視頻: Lab Solution: Time series prediction: end-to-end (rnn)
- 視頻: Deep RNNs
- 視頻: Lab Intro: Time series prediction: end-to-end (rnn2)
- 視頻: Lab Solution: Time series prediction: end-to-end (rnn2)
- 視頻: Improving our Loss Function
- 視頻: Demo: Time series prediction: end-to-end (rnnN)
- 視頻: Working with Real Data
- 視頻: Lab Intro: Time Series Prediction - Temperature from Weather Data
- 視頻: Lab Solution: Time Series Prediction - Temperature from Weather Data
- 視頻: Summary
已評分: Qwiklabs – Time series prediction: end-to-end (rnn)
已評分: Qwiklabs – Time series prediction: end-to-end (rnn2)
已評分: Qwiklabs – Time Series Prediction - Temperature from Weather Data
已評分: Dealing with Longer Sequences
第 2 週
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
- 視頻: Text Classification
- 視頻: Selecting a Model
- 視頻: Lab Intro: Text Classification
- 視頻: Lab Solution: Text Classification
- 視頻: Python vs Native TensorFlow
- 視頻: Demo: Text Classification with Native TensorFlow
- 視頻: Summary
已評分: Qwiklabs – Lab Solution: Text Classification
已評分: Text Classification
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
- 視頻: Modern methods of making word embeddings
- 視頻: Introducing TensorFlow Hub
- 視頻: Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub
- 視頻: Lab Solution: TensorFlow Hub
- 視頻: Using TensorFlow Hub within an estimator
已評分: Qwiklabs – Evaluating a pre-trained embedding from TensorFlow Hub
已評分: Reusable Embeddings
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
- 視頻: Attention Networks
- 視頻: Training Encoder-Decoder Models with TensorFlow
- 視頻: Introducing Tensor2Tensor
- 視頻: Lab Intro: Cloud poetry: Training custom text models on Cloud ML Engine
- 視頻: Lab Solution: Cloud poetry: Training custom text models on Cloud ML Engine
- 視頻: AutoML Translation
- 視頻: Dialogflow
- 視頻: Lab Intro: Introducing Dialogflow
- 視頻: Lab Solution: Dialogflow
已評分: Qwiklabs – Evaluating a pre-trained embedding from TensorFlow HubCloud poetry: Training custom text models on Cloud ML Engine
已評分: Qwiklabs – Getting Started with Dialogflow
已評分: Encoder-Decoder Models
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, 1 個閱讀材料
- Reading: Additional Reading
常見問題解答
運作方式
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製作方
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.
價格
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
評分和審閱
Quite a challenging course so far.