糖心vlog官网观看

As always - excellent lectures by Andrew Ng. However, I think that the programming assignments tend to be a it too easy and a bit too much "copy/paste".

Really enjoyed the course. Only suggestion is to talk about the programming side more in the lectures but overall I'm really happy Thank you very much.

In the last exercise of last week, we have to use TensorFlow v1 which was quite annoying if you already have learned tf v2 other than that great course

I wish prof. Ng provided more intuitions into underlying math particularly why gradient optimization techniques help. But like it anyways, very useful!

Great material, few minor errors that need fixing throughout. Noted in forums. I expect this will improve as more take the course and feedback applied.

Week 3 programming assignment required notions not touched in the training or mentioned in the instructions. Required lot of time to find information.

This course has to be updated!!!!! TF 2.0 is what we are using now, and especially for new users, it is important to start from the newest frameworks.

I missed in the course an explanation and possibly a programming assignment of different tuning algorithms, such as random search and Bayesian search.

The coverage on the subject was adequate but it will really help to make a pdf supporting document to highlight the hyper parameter tunning approach.

Probably a little too follow your nose at this point in the specialisation. But none-the-less very good. Would give 4.5 stars if that were an option.

Still some flaws + inaccuracies + video sequences that should be cut out. I think the organizers should really do it as people are now paying for it!

Last Tensorflow assignment has some output typos and bugs when using operators like @ and +. Course was ok, but that assignment took me way too long.

Great course to deepen my knowledge after first course. However, I would like to access more programming exercise for practice. That will be perfect!

I think this course would benefit from a little more explaining. There are a lot of new concepts and some explanations were too quick in my opinion.

The introduction to TF felt rushed, but maybe that's to be expected given the goals of this Course, which didn't include a kickstart of learning TF

A very good course but I had high hopes for a practical session (Assignment) with application of hyperparameter tuning, with Tensorflow or without.

孝械芯褉懈褟 褏芯褉芯褕邪褟, 薪芯 锌褉邪泻褌懈泻懈 胁芯 锌械褉胁褘褏 屑邪谢芯, 胁芯 胁褌芯褉褘褏 薪邪 褍褋褌邪褉械胁褕械泄 胁械褉褋懈懈 褌械薪蟹芯褉褎谢芯褍. 袧芯 蟹邪褌芯 胁 褌械芯褉懈懈 胁褋械 褉邪蟹卸械胁邪谢懈. 袨褔械薪褜 泻褉褍褌芯 胁褋械 芯斜褗褟褋薪褟械褌褋褟.

Enjoyed this course, especially the material that goes a bit deeper (different optimization methods, parameter tuning) and the intro to TensorFlow.

Less exciting than the first course, but this course is important to understanding the parameters that could affect a neural network's performance.

I feel like the course is a bit too easy. I would recommend making it more difficult by letting the end-user try more hyperparameter tuning tests.

what I did recognize in the deeplearning specialization that there are now further reading suggestions or reading syllabus like the other courses.

This course is one of the best course for good understanding of hyperparameter tunning.

And also let you know the effect of various hyperparameter.

Nice course, TensorFlow might need some more in-detail explanation because it's a different than programming with Python, but it was really nice.

Nice course and nice the Tensorflow introduction, but there are errors on the lecture and on the final test. Be aware to use the forum some times

Course was awesome, but there is an error with the grader for one of the programming assignments that took some time to search for a workaround.