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Отзывы учащихся о курсе Image Understanding with TensorFlow on GCP от партнера Google Cloud

4.6
Оценки: 237
Рецензии: 27

О курсе

This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow...

Лучшие рецензии

PR

Jul 24, 2019

Amazing course! Definitely recommend the course for learning Google's way to handle images! ;)

BS

Jan 23, 2019

It was One of the great course having labs which was really fun

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1–25 из 27 отзывов о курсе Image Understanding with TensorFlow on GCP

автор: vincent p

Feb 16, 2019

Several quicklabs issues

TPU quicklabs does not work. Always getting access error. I added manually the rights, but then I have an error about import apache-beam.

AutoML Vision quicklabs needs to mention to enable per object ACL or you cannot set the ACL.

Datalab is very very slow to start, very painful.

автор: Konstantinos S

Mar 29, 2019

Most labs don't work or are pointless

автор: Jakub B

Jun 26, 2019

Subscribing to this course only gives you option to run assignments on Qwik labs, and they're very poor for these kinds of assignments. You won't get any feedback on assignments anyway since there is no grader.

If you want to check out the material it's better to just clone training-data-analyst from github and do these assignments on GCP free tier.

автор: Please

Dec 07, 2018

思路很清晰,每一个课程讲解的都很明白,本觉得课程内容有些少,对于很多概念讲解的很浅显,但是想一下和门课主要是讲TensorFlow的,也就不这么苛刻了,老师语速适中,不十分推荐新手,尤其是英语不是特别棒的新手观看,少许有一些图像相关的了解看起来会快一些,还有就是觉得实验方面不是很好,主要还是感觉这种形式的实验其实自己在电脑上就可以做了,没必要很复杂的在GoogleCloud上费时间,希望改进一下Lab吧

автор: Raja R G

Dec 09, 2018

Great learning on Image ML models...

автор: Luiz G M

Nov 25, 2018

Great Course!

автор: Jun W

Nov 08, 2018

An excellent course. Clear, concise and comprehensive.

автор: Anjani K S

Nov 28, 2018

Really interesting labs in this course!.Thank you!

автор: Mark D

Jan 21, 2019

Was worried this would be just another CNN course but it was so much more. Showing out to use existing models etc. The details on CNN is a little lite but that can be found elsewhere. What was really good was the batch normalization and using pre-trained models but just changing the dense layers to provide classification,

автор: bhadresh s

Jan 23, 2019

It was One of the great course having labs which was really fun

автор: Facundo F

Mar 16, 2019

Excelent in every aspect. contents, coding, pacing. awesome

автор: Harold L M M

Nov 16, 2018

Very good course on CNNs. The labs were cool. Thank you!

автор: ELINGUI P U

Sep 21, 2018

A very good course, with cutting edge research about Deep Learning, Go google :-) !

автор: Atichat P

Oct 05, 2018

Good

автор: 林佳佑

Nov 02, 2018

this Courser teach a ongoing technique in GCP, and the world

The AUTOML is fascinated technique for learning

автор: Gregory R G J

Apr 30, 2019

Thumbs Up!

автор: Abdul R Y

Mar 26, 2019

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автор: SOYOUNG J

Jun 26, 2019

Good

автор: Putcha L N R

Jul 24, 2019

Amazing course! Definitely recommend the course for learning Google's way to handle images! ;)

автор: Daniel L

Aug 12, 2019

The code in the labs needs updating.

автор: Carlos V

Dec 09, 2018

The course provides an excellent overview of Image Understanding with TF and the utilization of all the capabilities of GCP to build productionable image systems.

автор: Roopesh N

Dec 23, 2018

Good Practical Experience with the concepts what that I learned . Good for recommending my friends.

автор: Hemant D K

Nov 28, 2018

Good material

автор: Mirko J R

Apr 04, 2019

You should improve the explanation of Transfer Learning from prebuilt models like ResNet. The conceptual side is not clear.

автор: Armando F

May 18, 2019

Highly recommended