Chevron Left
返回到 Recommendation Systems with TensorFlow on GCP

學生對 Google 云端平台 提供的 Recommendation Systems with TensorFlow on GCP 的評價和反饋

4.5
445 個評分

課程概述

In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series....

熱門審閱

DL

2019年8月19日

I enjoyed this course too much, usually every company wants a recommended system, but the courses or examples available on the web are few. Very well explained many theoretical aspects.

JA

2020年3月25日

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

篩選依據:

76 - Recommendation Systems with TensorFlow on GCP 的 79 個評論(共 79 個)

創建者 Abraham T

2021年5月30日

The Qwiklabs materials are outdated, however the lectures are insightful.

創建者 Kai W

2022年1月17日

Outdated (TF, GCP)

創建者 Aldrich L

2021年10月25日

The first part on content-based systems was pretty good, but everything after that was a mess. The second instructor (Ryan) was talking way too fast, and it felt like he was rushing everything he was explaining, and it would've been alright if his explanations were, at least, comprehensive enough. The problem is, there wasn't much groundwork in the course to build a good foundation for the students; they just did a brief introduction to the concepts, then rushed through the code implementation. Slowing down the videos did not help at all; it actually made it worse. The labs are another story, but then everyone else seems to be complaining about that, as well.

This is the only course in both specializations (ML on GCP and Advanced ML) that I didn't like.

創建者 Walter H

2021年6月6日

while the topics and lectures are very interesting, the course is extremely broken in its current form. There are multiple instances where you first get a lecture and are then asked to do a lab, but the lab is on a completely different topic than the lecture was. One example is the final lab of week 2, where you should be building an end to end solution, but instead you get a lab that only focusses on a topic from week 1. It seems this course was reworked at some point, but the 2nd version is no longer coherent whatsoever. It's hard to recommend this course in its current form as a result.