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Вернуться к Recommendation Systems with TensorFlow on GCP

Отзывы учащихся о курсе Recommendation Systems with TensorFlow on GCP от партнера Google Cloud

Оценки: 379
Рецензии: 64

О курсе

In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation engine • Build a hybrid recommendation engine with user and content embeddings >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...

Лучшие рецензии

25 мар. 2020 г.

Amongst all tensorflow courses this is probably the most useful. Using AI to make better and automated recommendations can benefit most businesses.

13 июля 2020 г.

This course and specialization are a great way to learn how to use the Google Cloud Platform with Tensorflow to build cutting edge systems.

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1–25 из 64 отзывов о курсе Recommendation Systems with TensorFlow on GCP

автор: Théodore M

18 мая 2020 г.

The courses is not up to date. They are using TF1 instead of TF2 and sometime python2. The WALS is not supported in TF2 and it is the central algorithm for collaborative filtering. I only learned that I should NOT build a recommendation system using TF,

автор: Maxim

5 июля 2019 г.

One star, but not to content. But because the course don't have "Audit" option. It's mean that after subscription ended and you received certificate, You can't more access to video material in course. When subscription active, You can use mobile application and download video material for studying offline. Before yours subscription ened, copy video material to safe place for later review.


But the course content deserves a higher mark - 4-5 stars.

автор: Liang-Chun C

16 нояб. 2018 г.

Not very intuitive explanation compared with previous four courses.

автор: Paulina M

30 мар. 2020 г.

Overall a good and comprehensive introduction to recommendation systems.

On the downside, some functions used were deprecated, there was sometimes inconsistency between versions in labs (for example automatic upgrading to Tensorflow 2.0, which was incompatible with other libraries used in the lab and things like that).

Also, in my opinion an insight into the models' results is lacking. There was a nice explanation of the performance in content-based part, but later during hybrid and context-aware systems there was no comment on models' accuracy in comparison to the original basic solution.

автор: Vu H Q

1 июня 2020 г.

you should move into tensorflow 2 instead of tf 1.x

автор: Jesper O

14 мар. 2019 г.

The labs by themselves - 'jupyter' notebooks - are good, but they were obviously developed in some other context and then reused in coursera. This is a problem. There about 6 labs per course - in each of the 10 courses of the two Machine Learning specialisations. Each lab starts the same way - connect to the google cloud, allocate a vm, check out a git repository - exact same repository for all labs. It takes 10 minutes. Not 10 minutes where you can go away and have a cup of coffee - 10 minutes where you have to be there and accept terms, answer 'Y' etc. If the labs are done outside the Coursera context you would be able to pick up where you left off in the previous lab - zero setup time. But not here - it is too much wasted time: 10*6*10=600 minutes. Evil.

автор: Ashley B

7 мая 2020 г.

The course lectures were decent, but the labs are full of bugs and erroneous or incomplete instructions. The final lab of the course (and also the specialization) has been unavailable for a few days now and tech support has not been helpful.

автор: Muhammad S U

29 мая 2020 г.

Most of the coding exercises use TF 1.X which is rather dated. I was hoping it would use TF 2.X instead. Also the coding exercises do not leave anything for the students to do. The video instructions were clear and well done.

автор: Nicolas S

8 июня 2020 г.

This one was the hardest of the specialization. A schema accompanying the code explanation would have been useful.

автор: José C L A

16 июня 2020 г.

The lectures excellent but the exercises were tedious and boring. Code only to be understand by the creator only.

автор: Sanjay K

12 янв. 2019 г.

No tensorflow.. lot of talk not a single math.. NOt good

автор: Jakub B

26 июня 2019 г.

Very poor course. Assignments are very weak and they do not test anything - there is no grader, you can just verify solution by watching the lab videos.

The content is OK, but web is full of good content on recommendation systems.

If you want to take this course by any means do not pay for it - by paying you only get access to qwiklab platform which sucks for these kind of assignments, and anyway you can do almost everything from the course on GCP free tier, and also not lose your progress every time you log out of lab.

автор: Brice T

4 авг. 2020 г.

Some labs with bigquery and the movieLens are not working - including the solutions, which is really time consuming and frustrating.

Labs illustrate very well the concepts and clarify the practical issues and solutions with gcp & tf. Excellent teaching !

автор: Dong Z

2 сент. 2020 г.

Before the DAG part everything was quite understandable and useful. I am completely lost with the DAG and cloud composer part. Maybe it is not a very good idea to teach some tool that is still under developing.

автор: YUJIE M

28 сент. 2020 г.

For this course, some part could be break into small chunk, and explain more detail. Generaly, this course is awesome!

автор: Fenrir

20 апр. 2019 г.


автор: Mark Z

17 июня 2019 г.

Bad course overall. It has some theoretical content in it, but concepts are not explained in depth and videos are sometimes hard to follow. Speaking of assignments, I had no motivation for completing them, because, firstly, they are not graded, and secondly, they are terribly designed and one won't get much from them.

автор: David K

22 апр. 2019 г.

Harder to follow than the other courses and did not love the teacher who led most of the lectures

автор: Rubens Z

21 июня 2020 г.

I love math, but unnecessary complexity was added to the content, making the course unpleasant to follow.

автор: Sinan G

7 янв. 2019 г.

Great work by Google, a lot of material and system walk-throughs. Apache Airflow / Google Composer is a smart tool but perhaps too complicated where more simple e.g. bash cron scripts could suffice - however it is understood that for truly scalable end-to-end systems the traditional single-cloud-virtual-machine solutions will not do. We are shown how that could look like and much more.

автор: Harold M

28 нояб. 2018 г.

This was a large and hard course on ML and in particular for Recommendation Systems. The videos were way to long. The content was very interesting. I've learned new algorithms like WALS for Collaborative Filtering and others more.

The Cloud Composer technology is cool for Keeping your System learning all the time.

Thank you Googlers.

автор: Rıdvan S

15 апр. 2020 г.

Course is really good. If you have some knowledge about recommendation systems but you don't know how you can realize it, this course is completely for you. However, some examples in labs don't work. Generally, fails on examples are about dependencies. But, I think, instuctors should ensure that these examples work.

автор: Jun W

17 нояб. 2018 г.

An excellent course. Frankly to say, I did not fully understand the details of this specialization. But it let me get a general idea what Google is doing. GCP has a lot of cool staff, and definitely has a bright future. Thank you Googlers.

автор: Carlos V

16 февр. 2019 г.

Excellent Course, in particular, the explanations around Google's Cloud Composer, the quality of the templates and the labs, thanks very much Lack and all your team for putting together this great specialization and course.

автор: Navid K

5 февр. 2020 г.

Amazing, Amazing Amazing course and specialisation. Definitely one of the best out there, if not The best!!

practical, advanced and real-world examples, particularly I loved You Tube example.

Another great job from Google