We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine.
이 강좌는 Machine Learning with TensorFlow on Google Cloud Platform 전문 분야의 일부입니다.
Intro to TensorFlow
제공자:
About this Course
최근 조회 57,220회
학습자 경력 결과
33%
이 강좌를 수료한 후 새로운 경력 시작하기
38%
이 강좌를 통해 확실한 경력상 이점 얻기
귀하가 습득할 기술
Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
학습자 경력 결과
33%
이 강좌를 수료한 후 새로운 경력 시작하기
38%
이 강좌를 통해 확실한 경력상 이점 얻기
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 3번째 강좌:
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
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.
완료하는 데 7분 필요
2개 동영상 (총 7분)
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.
완료하는 데 3시간 필요
19개 동영상 (총 72분)
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
완료하는 데 4시간 필요
Estimator API
In this module we will walk you through the Estimator API.
완료하는 데 4시간 필요
18개 동영상 (총 67분)
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
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.
완료하는 데 2시간 필요
6개 동영상 (총 29분), 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.
완료하는 데 2분 필요
1개 동영상 (총 2분)
1개의 동영상
Summary2m
검토
4.5
INTRO TO TENSORFLOW의 최상위 리뷰
LL 제공Aug 30, 2019
The tools and methods presented were great. The instructors were also fantastic.\n\nHowever the coding exercises were lacking in guidance even though the complete solution is given in the video.
DW 제공Oct 16, 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!
TW 제공Mar 30, 2020
This needs some updating - looks like the Tensorboard is no longer accessed in the same way as it was when this course was produced.\n\nOtherwise great! Good challenges! :/
YG 제공Apr 4, 2019
The procedure to connect to the cloud datalab was time consuming to do it every time.\n\nSuggestion : More topics in Core Tensorflow could be added. I enjoyed the course!
SS 제공Jun 5, 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.
MK 제공Mar 8, 2020
The course covers quite a few concepts -- TF basics, TF estimator, Google Cloud ML. It would be easier if the material is split into TF and Google Cloud lessons.
TB 제공Oct 6, 2018
Great course as an introduction to TF, however, the labs are not as in depth as I'd have liked. Nonetheless, the course is well executed by the presenters.
AH 제공Aug 10, 2018
Pretty helpful in getting to know the various levels of abstractions of tensorflow API and avoiding various pitfalls while building the Tensorflow model
SR 제공Jun 14, 2018
very useful for getting up to speed on tensorflowa little inconvenient that you have to start the infrastructure for each lab, takes minutes (5-7)
MR 제공Jan 6, 2019
Excellent course!!! It is the best way to really understand TensorFlow and getting to know its real potential. Thanks to all the instructors!!!
PS 제공Aug 5, 2019
Course is Best so far i learnt in Tensor Flow . It has all modules and content to be successful in Deep Learning and GCP Machine learning .
PR 제공Dec 26, 2018
Amazing course! The short length of videos makes it lot easier for students to follow! Google is honestly the best at whatever it does! :)
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.
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 <
자주 묻는 질문
등록 전에 강좌를 미리 볼 수 있나요?
예. 등록하기 전에 첫 번째 비디오를 미리 보고 강의 계획을 검토할 수 있습니다. 미리 보기에 포함되지 않은 콘텐츠를 이용하려면 강좌를 구매해야 합니다.
강의 및 과제를 언제 이용할 수 있게 되나요?
세션 시작일 전에 강좌에 등록하면 해당 강좌의 모든 강의 비디오 및 읽기 자료에 접근할 수 있습니다. 수업이 시작되면 과제를 제출할 수 있습니다.
등록하면 무엇을 이용할 수 있나요?
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.
강좌 수료증을 언제 받게 되나요?
강좌를 성공적으로 수료하면 전자 강좌 수료증이 성취도 페이지에 추가됩니다. 해당 페이지에서 강좌 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다.
이 강좌를 청강할 수 없는 이유는 무엇인가요?
이 강좌는 현재 Coursera에서 수업료를 결제했거나 재정 지원(해당하는 경우)을 받은 학습자만 이용할 수 있는 강좌입니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
