About this Course
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다음 전문 분야의 4개 강좌 중 4번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

완료하는 데 약 8시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

배울 내용

  • Check

    Solve time series and forecasting problems in TensorFlow

  • Check

    Prepare data for time series learning using best practices

  • Check

    Explore how RNNs and ConvNets can be used for predictions

  • Check

    Build a sunspot prediction model using real-world data

귀하가 습득할 기술

ForecastingMachine LearningTensorflowTime Seriesprediction

다음 전문 분야의 4개 강좌 중 4번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

완료하는 데 약 8시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

Course을(를) 수강하는 학습자

  • Data Scientists
  • Machine Learning Engineers
  • Traders
  • Chief Technology Officers (CTOs)
  • Risk Managers

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

Sequences and Prediction

10개 동영상 (총 33분), 3 readings, 3 quizzes
10개의 동영상
Time series examples4m
Machine learning applied to time series1m
Common patterns in time series5m
Introduction to time series4m
Train, validation and test sets3m
Metrics for evaluating performance2m
Moving average and differencing2m
Trailing versus centered windows1m
Forecasting4m
3개의 읽기 자료
Introduction to time series notebook10m
Forecasting notebook10m
Week 1 Wrap up10m
1개 연습문제
Week 1 Quiz
2
완료하는 데 3시간 필요

Deep Neural Networks for Time Series

10개 동영상 (총 27분), 5 readings, 3 quizzes
10개의 동영상
Preparing features and labels4m
Preparing features and labels3m
Feeding windowed dataset into neural network2m
Single layer neural network2m
Machine learning on time windows37
Prediction2m
More on single layer neural network2m
Deep neural network training, tuning and prediction4m
Deep neural network3m
5개의 읽기 자료
Preparing features and labels notebook10m
Sequence bias10m
Single layer neural network notebook10m
Deep neural network notebook10m
Week 2 Wrap up10m
1개 연습문제
Week 2 Quiz
3
완료하는 데 3시간 필요

Recurrent Neural Networks for Time Series

10개 동영상 (총 20분), 5 readings, 3 quizzes
10개의 동영상
Conceptual overview2m
Shape of the inputs to the RNN2m
Outputting a sequence1m
Lambda layers1m
Adjusting the learning rate dynamically2m
RNN1m
LSTM1m
Coding LSTMs2m
More on LSTM1m
5개의 읽기 자료
More info on Huber loss10m
RNN notebook10m
Link to the LSTM lesson10m
LSTM notebook10m
Week 3 Wrap up10m
1개 연습문제
Week 3 Quiz
4
완료하는 데 3시간 필요

Real-world time series data

11개 동영상 (총 24분), 5 readings, 3 quizzes
11개의 동영상
Convolutions58
Bi-directional LSTMs3m
LSTM1m
Real data - sunspots3m
Train and tune the model3m
Prediction1m
Sunspots1m
Combining our tools for analysis3m
Congratulations!38
Specialization wrap up - A conversation with Andrew Ng2m
5개의 읽기 자료
Convolutional neural networks course10m
More on batch sizing10m
LSTM notebook10m
Sunspots notebook10m
Wrap up10m
1개 연습문제
Week 4 Quiz
4.6
103개의 리뷰Chevron Right

Sequences, Time Series and Prediction의 최상위 리뷰

대학: ORAug 4th 2019

It was an amazing experience to learn from such great experts in the field and get a complete understanding of all the concepts involved and also get thorough understanding of the programming skills.

대학: YKSep 30th 2019

A step by step explanation of how to use TensorFlow 2.0 for building a Neural network for sequences and time series. With detailed examples of code and of how to choose hyper-parameters.

강사

Avatar

Laurence Moroney

AI Advocate
Google Brain

deeplearning.ai 정보

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

TensorFlow in Practice 전문 분야 정보

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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