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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최근 조회 20,716회
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다음 특화 과정의 5개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
고급 단계
완료하는 데 약 15시간 필요
영어
자막: 영어
학습자 경력 결과
50%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
67%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 5개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
고급 단계
완료하는 데 약 15시간 필요
영어
자막: 영어
제공자:
Google 클라우드
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.
강의 계획 - 이 강좌에서 배울 내용
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.
완료하는 데 4시간 필요
14개 동영상 (총 41분), 1 개의 읽기 자료, 4 개의 테스트
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.
완료하는 데 15분 필요
4개 동영상 (총 15분)
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.
완료하는 데 4시간 필요
14개 동영상 (총 62분)
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
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.
완료하는 데 2시간 필요
8개 동영상 (총 35분)
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.
완료하는 데 1시간 필요
6개 동영상 (총 28분)
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.
완료하는 데 3시간 필요
10개 동영상 (총 84분)
10개의 동영상
Attention Networks4m
Training Encoder-Decoder Models with TensorFlow6m
Introducing Tensor2Tensor11m
Lab Intro: Cloud poetry: Training custom text models on Cloud AI Platform1m
Lab Solution: Cloud poetry: Training custom text models on Cloud AI Platform25m
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.
완료하는 데 14분 필요
1개 동영상 (총 4분), 1 개의 읽기 자료
1개의 동영상
Summary3m
1개의 읽기 자료
Additional Reading10m
검토
SEQUENCE MODELS FOR TIME SERIES AND NATURAL LANGUAGE PROCESSING의 최상위 리뷰
PR 제공2019년 8월 10일
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!
JW 제공2018년 11월 10일
Excellent course for those who know RNN. Knowledge is refreshed and techniques are consolidated. More details about Google ecosystem is introduced.
ND 제공2020년 2월 28일
I have only one remark, please improve lesson regarding NLP by includin model build etc. Otherwise course is excellent and was helpful for me
HS 제공2019년 7월 22일
Though not focused on fundamental concepts, it's a great course to learn to use tensorflow and google cloud platform for sequence modelling.
Advanced Machine Learning with TensorFlow on Google Cloud Platform 특화 과정 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.
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