糖心vlog官网观看

Great Introduction for new learner with some labs experience.

Course content was good, labs were buggy and frustrating

A challenging and very satisfying course! Recommended!!

Cours recommand茅 pour les d茅butant en computer vision

A broad rather than deep course. I will give it a 4.

This course more focuses on ibm watson than opencv.

Very organized and well designed for beginners.

It was great course I got valuable information

The Open CV module is not working corectly

verifying my id is really time consuming

could have subtitles for more languages

There are so many errors in Labs

Nice course for Computer Vision

it was an informative course

EXCELLENT COURSE CONTENT

Good course, i liked CV

Bugs on the platform.

Its a good

Good

Cool

Good

The material is very useful and is presented well in the videos. The labs with only Jupyter Notebooks work well. The labs with CV Studio were not always successful. First, the instructions for using CV Studio in the assignments need to be updated and clarified. Once you get past the differences in the various sets of instructions and get something working with CV Studio, the labs with only the Jupyter notebooks work well. However, the labs in which you're asked to test your trained model with their "one-click app" do not. For these labs, you train the model with a Jupyter notebook (which worked well) and deploy the model in a one-click app, but when CV Studio says the one-click app is "ready", clicking the link lands you on a page saying that you'll be redirected to the webpage when it's ready but it NEVER redirects. I wish that I had known that from the start. I re-did these labs multiple times because I assumed that I was the one making mistakes. However, the final project appears to have been changed to use a Jupyter notebook instead of deploying a one-click app. If they re-work the labs either to include better instructions for using CV Studio while avoiding the one-click app or to avoid CV Studio all together, the course would be better.

The videos are high quality, however there was some kind of glitch that requires changing the video setting from 720p to something lower, then back, so that you can at least see the video and understand the content - 糖心vlog官网观看 should have this fixed. A lot of the labs need updating, to match the hosted support systems. There were a lot of deprecation warnings when running the notebook labs and the screen shots students are supposed to use for reference in the IBM labs are out of date with the updated software running there, leaving students to guess what to do next (unrelated to the assignment at hand). The applications would not publish as expected on the IBM Watson site either -- had to do workarounds and complete the final assignment by just running classifications straight out of the Jupyter notebook, and not through the web app as designed. Final assignment was also not clear on what images to process for the final grade and surprises students with much more additional work to search for images online that cannot be controlled or verified for the final grade -- which is 25%. This experience could have been done better. Please redesign it.

While the course is mostly good, it does have some pretty large issues that might cause problems: 1. A large portion of the material is on a different app, which unfortunately is not intuitive to use. It took too long to realise that the code snippets can be run natively in the text file they are presented in. 2. The videos go from 0 to 100 a bit too quickly. It feels like the teacher just wants to get the facts out as fast as possible and doesn't attempt to make sure everything is understood. 3. The grading and content don't match. The tests ask pretty simple questions about roughly 5% of the material and the remaining 95% are untouched. It feels like that 95% is surplus. All in all, the course is a pretty good intro into the basic theory, but not a good way to learn how to do the stuff yourself unless you the exact same tools as the material is in.

I took this course as part of IBM AI Engineering certification. The course should have been done after course for PyTorch as more than 50% of labs are with it. Initial labs are okish, latter ones are much better. But learning videos are with audio that is AI generated, theoretical explanations are thin and could better cover topics from the labs (some are not even covered at all). Luckily there is YouTube and internet. Course is more of exercise than really challenging. Peer graded assignment is interesting as focus is not writing code, but finding examples that do not work and how they can potentially be made to work.