About this Course
최근 조회 51,825

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 10시간 필요

권장: 2-3 weeks of study, 8-10 hours/week...

영어

자막: 프랑스어, 포르투갈어 (브라질), 독일어, 영어, 스페인어, 일본어...

귀하가 습득할 기술

Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 10시간 필요

권장: 2-3 weeks of study, 8-10 hours/week...

영어

자막: 프랑스어, 포르투갈어 (브라질), 독일어, 영어, 스페인어, 일본어...

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 7분 필요

Introduction

The tool we will use to write machine learning programs is TensorFlow and so in this course, we will introduce you to TensorFlow. In the first course, you learned how to formulate business problems as machine learning problems and in the second course, you learned how machine works in practice and how to create datasets that you can use for machine learning. Now that you have the data in place, you are ready to get started writing machine learning programs.

...
2 videos (Total 7 min)
2개의 동영상
Intro to Qwiklabs5m
완료하는 데 3시간 필요

Core TensorFlow

We will introduce you to the core components of TensorFlow and you will get hands-on practice building machine learning programs. You will compare and write lazy evaluation and imperative programs, work with graphs, sessions, variables, as finally debug TensorFlow programs.

...
19 videos (Total 72 min), 4 quizzes
19개의 동영상
What is TensorFlow2m
Benefits of a Directed Graph5m
TensorFlow API Hierarchy3m
Lazy Evaluation4m
Graph and Session4m
Evaluating a Tensor2m
Visualizing a graph2m
Tensors6m
Variables6m
Lab Intro: Writing low-level TensorFlow programs16
Lab Solution8m
Introduction5m
Shape problems3m
Fixing shape problems2m
Data type problems1m
Debugging full programs4m
Intro: Debugging full programs15
Demo: Debugging Full Programs3m
3개 연습문제
What is TensorFlow?2m
Graphs and Sessions8m
Core TensorFlow20m
2
완료하는 데 4시간 필요

Estimator API

In this module we will walk you through the Estimator API.

...
18 videos (Total 67 min), 4 quizzes
18개의 동영상
Estimator API3m
Pre-made Estimators5m
Demo: Housing Price Model1m
Checkpointing1m
Training on in-memory datasets2m
Lab Intro: Estimator API39
Lab Solution: Estimator API10m
Train on large datasets with Dataset API8m
Lab Intro: Scaling up TensorFlow ingest using batching35
Lab Solution: Scaling up TensorFlow ingest using batching5m
Big jobs, Distributed training6m
Monitoring with TensorBoard3m
Demo: TensorBoard UI28
Serving Input Function5m
Recap: Estimator API1m
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7m
1개 연습문제
Estimator API18m
3
완료하는 데 2시간 필요

Scaling TensorFlow models

I’m here to talk about how you would go about taking your TensorFlow model and training it on GCP’s managed infrastructure for machine learning model training and deployed.

...
6 videos (Total 29 min), 1 reading, 2 quizzes
6개의 동영상
Why Cloud AI Platform?6m
Train a Model2m
Monitoring and Deploying Training Jobs2m
Lab Intro: Scaling TensorFlow with Cloud AI Platform50
Lab Solution: Scaling TensorFlow with Cloud AI Platform16m
1개의 읽기 자료
Cloud ML Engine is now Cloud AI Platform10m
1개 연습문제
Cloud AI Platform10m
완료하는 데 2분 필요

Summary

Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the Estimator API, and end with scaling your machine learning models with Cloud Machine Learning Engine.

...
1 video (Total 2 min)
1개의 동영상
4.4
159개의 리뷰Chevron Right

35%

이 강좌를 수료한 후 새로운 경력 시작하기

38%

이 강좌를 통해 확실한 경력상 이점 얻기

Intro to TensorFlow의 최상위 리뷰

대학: DWOct 17th 2018

pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!

대학: SSJun 6th 2018

Nice introduce, might be more on introduce the model structure, because I still need to read additional notes to locate how to train my deep learning model online.

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....

Machine Learning with TensorFlow on Google Cloud Platform 전문 분야 정보

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <...
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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