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Intro to TensorFlow(으)로 돌아가기

Google 클라우드의 Intro to TensorFlow 학습자 리뷰 및 피드백

4.5
1,951개의 평가
212개의 리뷰

강좌 소개

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. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine...

최상위 리뷰

DW

Oct 17, 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!

SS

Jun 06, 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.

필터링 기준:

Intro to TensorFlow의 208개 리뷰 중 101~125

교육 기관: Cesar R L S

Jan 18, 2020

Very good

교육 기관: Carlo B

Oct 03, 2019

Very nice

교육 기관: Víctor D L T

Jul 23, 2019

excelente

교육 기관: Nayanajith P

May 26, 2019

It's nice

교육 기관: borja v

Jun 17, 2019

Perfect!

교육 기관: Zhuqing X

May 04, 2019

Love it!

교육 기관: 영신 박

Apr 27, 2019

Awesome!

교육 기관: Terry L

Apr 26, 2019

이 과정을 끝ㄴ

교육 기관: Bielushkin M

Nov 11, 2018

good job

교육 기관: Sujeethan V

Mar 26, 2019

Amazing

교육 기관: Aldi N S

Jan 24, 2020

Great

교육 기관: Ahmad T

Aug 26, 2019

Great

교육 기관: Loganathan S

Aug 02, 2019

Good!

교육 기관: 江祖榮

Sep 19, 2019

Good

교육 기관: Fathima j

May 11, 2019

good

교육 기관: Dong H S

Apr 28, 2019

good

교육 기관: Atichat P

Jun 02, 2018

Good

교육 기관: Girish S K

Jul 22, 2019

The course was good introduction to tensor flow I learned lot of basics which otherwise I could not have learned from books or other online materials. The concepts are well explained. What I am not happy is about the Datascience labs. In places where internet is slow it is very difficult to do it. Instead of this in we are provided some alternate instructions to run them on a local machine that would have helped at least for some of the first few labs. I know that all of them cannot be run on local machine then the whole purpose of learning tensorflow on Google Cloud is defeated. The whole purpose is to learn how to run it on a cloud environment with scaling. I know that is not possible on a local machine. Another option would be to provide instructions to run the code with without notebook. I basically do not like notebooks , I Prefer command line to notebooks to execute and see results live. But overall I got a good intro about tensorflow - Thankyou very much.

교육 기관: Benny P

Dec 05, 2019

First of all we need to understand that TensorFlow is not just a Python toolkit. It's a complete tools from Python library, training management, monitoring, down to deployment to cloud or what have you. Therefore this course should be viewed as getting started introduction to ALL of that, not just the toolkit. And I think it's quite good. There are few glitches here and there when it comes to interacting with the GCP, but that's fine, you're learning something while fixing it. The disappointment comes from the forum though, as the staff's only response seem to be to shift the responsibility to Qwiklabs

교육 기관: Yaron K

Jul 14, 2018

An excellent introduction to TensorFlow, Including debugging tips, and how to scale up TensorFlow models and deploy them. So why only 4 stars ? because there is no audit option for this course and the videos can't be downloaded. Presumable the notebooks with sample code can be cloned from Github - but it seems the explanations will not be available unless you re-enroll. This policy is even more inexplicable considering that the course serves as a "presale" for the Google cloud platform.

교육 기관: David M B

Feb 26, 2019

Very useful but I had some problems with lab infrastructure. Options to create buckets wouldn't appear sometimes and I had to open and close google cloud console to make it work sometimes. Regarding the course it was great but there is a lot of boilerplate code and though the steps are simple and clear there is a lot to digest, I will need much more time master this TF/GCP workflow, but anyway this is a great start.

교육 기관: Sachin A

Jun 16, 2018

I think a lot of the lab-explanation given in the video following the qwiklab should be in the python notebook; make it a little more illustrative (e.g. architecture diagrams). Also, be a little more generous with the lab time - the last lab was too long (or perhaps change the code to select the faster ML option - standard/TPUs etc. to make the training go faster)

교육 기관: Zhenyu W

Jan 20, 2019

One of the lecturers should improve his English speaking. The course should add more contents, explanations, and exercises for the 3rd part of the course regarding how to scale TF models with CMLE, for example, some bash cmds or some code are confusing, unless this content will be covered more in the following courses.

교육 기관: Thibault D

Sep 10, 2019

I enjoyed this course a lot. If I could modify anything, I would adjust the content and pace of the third week. The videos are relatively simple to understand and well-explained while the final lab feels a lot harder with a lot of unknown command to execute.

교육 기관: Asmit M

Jul 30, 2019

hands on demonstrations were good. More in depth explanation can be done fro some of the codes including the part in which data fatching from the json file was explained, and the process to be followed in the gcp to make the model and deploy it.