课程信息
4.3
3 个评分
1 个审阅
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...
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Advanced Level

高级

Clock

Approx. 9 hours to complete

建议:20 hours/week...
Comment Dots

English

字幕:English...
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Advanced Level

高级

Clock

Approx. 9 hours to complete

建议:20 hours/week...
Comment Dots

English

字幕:English...

教学大纲 - 您将从这门课程中学到什么

Week
1
Clock
完成时间为 4 小时

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....
Reading
14 个视频(共 41 分钟), 1 个阅读材料, 4 个测验
Video14 个视频
Getting Started with Google Cloud Platform and Qwiklabs3分钟
Sequence data and models5分钟
From sequences to inputs2分钟
Modeling sequences with linear models2分钟
Lab intro: using linear models for sequences分钟
Lab solution: using linear models for sequences7分钟
Modeling sequences with DNNs2分钟
Lab intro: using DNNs for sequences分钟
Lab solution: using DNNs for sequences2分钟
Modeling sequences with CNNs3分钟
Lab intro: using CNNs for sequences分钟
Lab solution: using CNNs for sequences3分钟
The variable-length problem4分钟
Reading1 个阅读材料
How to send course feedback10分钟
Quiz1 个练习
Working with Sequences分钟
Clock
完成时间为 15 分钟

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....
Reading
4 个视频(共 15 分钟), 1 个测验
Video4 个视频
How RNNs represent the past4分钟
The limits of what RNNs can represent5分钟
The vanishing gradient problem1分钟
Quiz1 个练习
Recurrent Neural Networks分钟
Clock
完成时间为 4 小时

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....
Reading
14 个视频(共 62 分钟), 4 个测验
Video14 个视频
LSTMs and GRUs6分钟
RNNs in TensorFlow2分钟
Lab Intro: Time series prediction: end-to-end (rnn)分钟
Lab Solution: Time series prediction: end-to-end (rnn)10分钟
Deep RNNs1分钟
Lab Intro: Time series prediction: end-to-end (rnn2)分钟
Lab Solution: Time series prediction: end-to-end (rnn2)6分钟
Improving our Loss Function2分钟
Demo: Time series prediction: end-to-end (rnnN)3分钟
Working with Real Data10分钟
Lab Intro: Time Series Prediction - Temperature from Weather Data1分钟
Lab Solution: Time Series Prediction - Temperature from Weather Data11分钟
Summary1分钟
Quiz1 个练习
Dealing with Longer Sequences分钟
Week
2
Clock
完成时间为 2 小时

Text Classification

In this module we look at different ways of working with text and how to create your own text classification models. ...
Reading
8 个视频(共 35 分钟), 2 个测验
Video8 个视频
Text Classification6分钟
Selecting a Model2分钟
Lab Intro: Text Classification分钟
Lab Solution: Text Classification11分钟
Python vs Native TensorFlow4分钟
Demo: Text Classification with Native TensorFlow7分钟
Summary1分钟
Quiz1 个练习
Text Classification分钟
Clock
完成时间为 1 小时

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....
Reading
6 个视频(共 28 分钟), 2 个测验
Video6 个视频
Modern methods of making word embeddings8分钟
Introducing TensorFlow Hub1分钟
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub分钟
Lab Solution: TensorFlow Hub9分钟
Using TensorFlow Hub within an estimator1分钟
Quiz1 个练习
Reusable Embeddings分钟
Clock
完成时间为 3 小时

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....
Reading
10 个视频(共 84 分钟), 3 个测验
Video10 个视频
Attention Networks4分钟
Training Encoder-Decoder Models with TensorFlow6分钟
Introducing Tensor2Tensor11分钟
Lab Intro: Cloud poetry: Training custom text models on Cloud ML Engine1分钟
Lab Solution: Cloud poetry: Training custom text models on Cloud ML Engine25分钟
AutoML Translation4分钟
Dialogflow6分钟
Lab Intro: Introducing Dialogflow分钟
Lab Solution: Dialogflow13分钟
Quiz1 个练习
Encoder-Decoder Models分钟
Clock
完成时间为 14 分钟

Summary

In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data. ...
Reading
1 个视频(共 4 分钟), 1 个阅读材料
Video1 个视频
Summary3分钟
Reading1 个阅读材料
Additional Reading10分钟

关于 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....

关于 Advanced Machine Learning with TensorFlow on Google Cloud Platform 专项课程

>>>Look for details below for COMPLETION CHALLENGE and for an opportunity to receive a GCP t-shirt!<<< 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. COMPLETION CHALLENGE Complete any GCP specialization from October 23 through November 30, 2018 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details....
Advanced Machine Learning with TensorFlow on Google Cloud Platform

常见问题

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid. If you’d like to take this course, but can’t afford the course fee, we encourage you to submit a financial aid application.

还有其他问题吗?请访问 学生帮助中心