About this Course
24,820

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 11시간 필요

권장: 16 hours/week...

영어

자막: 영어

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 11시간 필요

권장: 16 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

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 videos (Total 41 min), 1 reading, 4 quizzes
14개의 동영상
Getting Started with Google Cloud Platform and Qwiklabs3m
Sequence data and models5m
From sequences to inputs2m
Modeling sequences with linear models2m
Lab intro: using linear models for sequences20
Lab solution: using linear models for sequences7m
Modeling sequences with DNNs2m
Lab intro: using DNNs for sequences19
Lab solution: using DNNs for sequences2m
Modeling sequences with CNNs3m
Lab intro: using CNNs for sequences19
Lab solution: using CNNs for sequences3m
The variable-length problem4m
1개의 읽기 자료
How to send course feedback10m
1개 연습문제
Working with Sequences
완료하는 데 15분 필요

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 videos (Total 15 min), 1 quiz
4개의 동영상
How RNNs represent the past4m
The limits of what RNNs can represent5m
The vanishing gradient problem1m
1개 연습문제
Recurrent Neural Networks
완료하는 데 4시간 필요

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 videos (Total 62 min), 4 quizzes
14개의 동영상
LSTMs and GRUs6m
RNNs in TensorFlow2m
Lab Intro: Time series prediction: end-to-end (rnn)45
Lab Solution: Time series prediction: end-to-end (rnn)10m
Deep RNNs1m
Lab Intro: Time series prediction: end-to-end (rnn2)26
Lab Solution: Time series prediction: end-to-end (rnn2)6m
Improving our Loss Function2m
Demo: Time series prediction: end-to-end (rnnN)3m
Working with Real Data10m
Lab Intro: Time Series Prediction - Temperature from Weather Data1m
Lab Solution: Time Series Prediction - Temperature from Weather Data11m
Summary1m
1개 연습문제
Dealing with Longer Sequences
2
완료하는 데 2시간 필요

Text Classification

In this module we look at different ways of working with text and how to create your own text classification models. ...
8 videos (Total 35 min), 2 quizzes
8개의 동영상
Text Classification6m
Selecting a Model2m
Lab Intro: Text Classification47
Lab Solution: Text Classification11m
Python vs Native TensorFlow4m
Demo: Text Classification with Native TensorFlow7m
Summary1m
1개 연습문제
Text Classification
완료하는 데 1시간 필요

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 videos (Total 28 min), 2 quizzes
6개의 동영상
Modern methods of making word embeddings8m
Introducing TensorFlow Hub1m
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub24
Lab Solution: TensorFlow Hub9m
Using TensorFlow Hub within an estimator1m
1개 연습문제
Reusable Embeddings
완료하는 데 3시간 필요

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 videos (Total 84 min), 3 quizzes
10개의 동영상
Attention Networks4m
Training Encoder-Decoder Models with TensorFlow6m
Introducing Tensor2Tensor11m
Lab Intro: Cloud poetry: Training custom text models on Cloud ML Engine1m
Lab Solution: Cloud poetry: Training custom text models on Cloud ML Engine25m
AutoML Translation4m
Dialogflow6m
Lab Intro: Introducing Dialogflow54
Lab Solution: Dialogflow13m
1개 연습문제
Encoder-Decoder Models
완료하는 데 14분 필요

Summary

In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data. ...
1 video (Total 4 min), 1 reading
1개의 동영상
1개의 읽기 자료
Additional Reading10m
4.5
16개의 리뷰Chevron Right

최상위 리뷰

대학: MDFeb 3rd 2019

Very good.The explanation of the RNN was very good but the tensor2tensor was very hard.

Google 클라우드 정보

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....

Advanced Machine Learning with TensorFlow on Google Cloud Platform 전문 분야 정보

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....
Advanced Machine Learning with TensorFlow on Google Cloud Platform

자주 묻는 질문

  • 예. 등록하기 전에 첫 번째 비디오를 미리 보고 강의 계획을 검토할 수 있습니다. 미리 보기에 포함되지 않은 콘텐츠를 이용하려면 강좌를 구매해야 합니다.

  • 세션 시작일 전에 강좌에 등록하면 해당 강좌의 모든 강의 비디오 및 읽기 자료에 접근할 수 있습니다. 수업이 시작되면 과제를 제출할 수 있습니다.

  • 등록 후 세션이 시작되면 읽기 자료 항목 및 강좌 토론 포럼을 포함하여 모든 비디오와 기타 리소스를 이용할 수 있습니다. 연습 평가를 보고 제출하며 필요한 성적 평가 과제를 완료하여 성적을 받고 강좌 수료증을 취득할 수 있습니다.

  • 강좌를 성공적으로 수료하면 전자 강좌 수료증이 성취도 페이지에 추가됩니다. 해당 페이지에서 강좌 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다.

  • 이 강좌는 현재 Coursera에서 수업료를 결제했거나 재정 지원(해당하는 경우)을 받은 학습자만 이용할 수 있는 강좌입니다.

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