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學生對 Google 云端平台 提供的 TensorFlow on Google Cloud 的評價和反饋

4.4
2,672 個評分
330 條評論

課程概述

This course covers designing and building a TensorFlow 2.x input data pipeline, building ML models with TensorFlow 2.x and Keras, improving the accuracy of ML models, writing ML models for scaled use and writing specialized ML models....

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VC

2020年5月17日

I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.

AJ

2020年11月14日

Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy').\n\nEvan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.

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51 - TensorFlow on Google Cloud 的 75 個評論(共 325 個)

創建者 PLN R

2018年12月27日

Amazing course! The short length of videos makes it lot easier for students to follow! Google is honestly the best at whatever it does! :)

創建者 Stephen R

2018年8月3日

Nice start. Have a look at Andrew NG's course, especially how they do the labs. Might give you some ideas for your next class versions.

創建者 Toufik K

2018年6月16日

The BEST course I've ever taken on TensorFlow! Definitely recommended to anyone interested in learning the fundamental of TF and GCP

創建者 Xuekun Y

2019年3月22日

Good explanation, It will be better if this class could provide slides so that we do not need to take screenshoot all the time...

創建者 Gbadebo B

2018年9月5日

I really enjoyed this course, i didnt know machine learning models can be deployed so easily, All thanks to tensor flow and CLME

創建者 Maurizio C

2018年9月24日

Excellent course, well explained.

I would like to recommend adding a lesson to train with input from external test file (CSV)

創建者 Meynardo J

2018年7月28日

Nice intro to TensorFlow in general, and a great explanation of the Estimator API and the Cloud Machine Learning Engine.

創建者 Mark K

2020年6月7日

This course is so really good to learn about the general knowledge and skill of TensorFlow with Google Cloud Platform.

創建者 Yonatan N

2019年2月18日

This was a great introduction to the Estimator API, which is a prefect place to start for new TensorFlow developers.

創建者 Jun W

2018年5月26日

A very good course. As I go deeper, I gradually figured out what a great job Google has done. I mean TensorFlow.

創建者 KAMASANI V R

2020年6月22日

This course helped me in knowing how to train a model using tensor flow and pitfalls in using it. Thank You.

創建者 Carl H

2018年6月3日

Labs are excellent. They give a good illustration of concepts but with the opportunity to go much deeper.

創建者 Bill F

2019年4月17日

You have to really follow up closely with coding in each video, as you will need the code in the labs.

創建者 Mohammed F

2019年7月6日

An interesting course on the basics TensorFlow Estimators and Google Cloud Machine Learning engine.

創建者 Gowthaman S

2020年4月9日

Very detailed overview and explanations on various Tensor Flow components and their internals.

創建者 Sidhant P

2019年3月7日

It really helped me to get a grasp of Tensorflow. Now things are quite understandable for me.

創建者 Thanh N N

2019年7月2日

Great course for beginning with TensorFlow! I learned useful things from the course! Thanks!

創建者 Sivaranjan G

2019年6月5日

This is indeed a great course on Tensorflow and Google cloud machine learning engine (CMLE).

創建者 Saibal M

2019年7月9日

Love this course. One of the best course in this specialization. Very informative content.

創建者 Angel C

2020年7月12日

Helped me get really comfortable using TensorFlow and thinking about the logic behind it.

創建者 Kyle K

2019年1月27日

The labs and videos provided a great framework to learn the key features of tensor-flow.

創建者 Ramprasad P

2020年12月21日

Very informative, clear & well structured course delivered by knowledgeable faculties

創建者 Gustavo M

2018年8月17日

Very good lectures to really take advantage of tensorFlow and Google Cloud's Services

創建者 Sashika N

2020年4月19日

Great course to get hands-on experience with GCP and TensorFlow. Highly recommended.

創建者 David R D

2018年9月6日

good introduction to tensorflow and also covers ML @ scale and production readiness.