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學生對 Google 云端平台 提供的 End-to-End Machine Learning with TensorFlow on GCP 的評價和反饋

993 個評分
151 條評論


In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization ( One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform Prerequisites: Basic SQL, familiarity with Python and TensorFlow >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...



Nov 18, 2019

awesome learning experience fro the teacher from google. thanks to coursera and google for providing me such a good lesson which will be beneficial for my upcoming future and research work


Mar 03, 2019

Definitely adds a unique perspective on thinking about machine learning systems at scale. This course is suitable for Data Scientists, Data Engineers and Machine Learning Engineers.


76 - End-to-End Machine Learning with TensorFlow on GCP 的 100 個評論(共 152 個)

創建者 Muhammad Z H

Sep 10, 2019

learned alot


Jun 23, 2019

Great Course

創建者 최철웅

Jun 16, 2019

V3RY 900D!!!

創建者 Parul G

Apr 07, 2019

Very Helpful

創建者 Kesepattapu N

Feb 22, 2019

nice course

創建者 장해수

Jun 16, 2019


創建者 Gregory R G J

Apr 13, 2019

Thumbs Up!

創建者 Jincheol W

Jun 25, 2019

Good Job!

創建者 Kim J W

Jun 18, 2019

very good

創建者 Björn S

Apr 11, 2019


創建者 Alexandros D N

Nov 13, 2018

Very good

創建者 이관동

Jul 02, 2019


創建者 이근주

Jul 01, 2019

so nice

創建者 김기원

Jun 28, 2019

is nice

創建者 황정용

Jun 24, 2019


創建者 Naman M

Sep 23, 2019


創建者 Lee S J

Jul 03, 2019


創建者 Lee M

Jul 01, 2019


創建者 황인규

Jul 03, 2019


創建者 이전규

Jun 23, 2019


創建者 Dennys R T

Jun 16, 2019


創建者 Atichat P

Oct 03, 2018


創建者 Manu G

Oct 04, 2019

Course covers the fundamentals of GCP with TF. Although the labs don't require much of a coding, and the ones which require have a poor structure because after each subtask say Task 1, you should be able to see if your code outputs the correct output, so for that they should have included some testcases. Also in the training part, quicklab has limit of 2 hrs, but training takes about 40-50 mins for a lower input size, and that lab requires to run training 3 times, so I was forced to just trim down the input size to fit all tasks within the lab time limit.

創建者 Mr. J

Sep 05, 2019

great survey of it. optional labs should be mandatory I think. Also it would be nice to have a end to end walk through in summation. another option to complete the mental model it to map notebook sections to the GCP infrastructure in a presentation.

I wonder about cloning the gcp repo locally to use it as a local template to further study. In other words I fire it up in my account later. or I access GCP via anaconda jupiter. Just wondering.