Jul 20, 2018
Fantastic course.In fact, I think it,s not a easy thing to accomplish all the assignments with this course.\n\nI got a lot of gains through this course. Thanks for all the instructors.
Mar 01, 2019
Really Great course. I would recommend everyone to take this course but after having some "basic knowledge" of Machine Learning, Deep Learning, CNN, RNN and programming in python.
대학: Murat Öztürkmen•
May 21, 2019
It is a well prepared course which includes lots of tips and trick and theoretical background to be successful.
대학: ashesh gajanan mishra•
May 09, 2019
Its much more informative than the title suggests. A good course to take for someone who already knows basics/theoretical knowledge of machine learning.
대학: Jun Kunikata•
May 02, 2019
Some programming assignments were not instructed enough, so it's very hard to solve them without discussion forums. But this is good course as a whole.
대학: Driaan Jansen•
Apr 29, 2019
The content of the course is really excellent, and the lecturers' knowledge is just superb.
The only drawback of the course is that the lecturers' native language is not English, and accordingly it is sometimes difficult to understand them. But there are subtext to the lectures in English that one can refer to.
대학: AGWU Elbby Skermine•
Apr 27, 2019
Learned and liked a lot
대학: Mohammed Saad Elsayed•
Apr 19, 2019
very detailed , clear and to the point , i loved it
대학: Erik Grabljevec•
Apr 13, 2019
This course gives a great overview of what can be done with DNNs. Topics are well chosen, clearly presented, and a good level of difficulty.
대학: Marian Lobur•
Apr 12, 2019
I'm not sure that this course is needed at all. Folks are trying to explain multiple architectures of Neural Networks, without giving an actual understanding why it works. Plus I have a feeling that all of this things are going to explained in next courses of this specialization.
대학: Swapnil Kumar Bishnu•
Apr 11, 2019
One of the best courses on deep learning . Kudos to the creators.
대학: Tina Zhu•
Apr 07, 2019
A few typos in the slides, quizzes and in the homework, some of the presenters do not speak very clearly and are hard to follow (which would not be a big problem if they practiced their lectures, cleaned up the transcripts, gave out notes or powerpoint slides.) Quality of the course is much lower than the Stanford ML course on this site.
Coursera Jupyter notebooks keep disconnecting and my computer has trouble training the computation-heavy homework as well. Some of the homework is literally 95% wait for the computer or Coursera notebook to run or restart, 5% actual coding. It makes homework incredibly slow and inefficient for learning. I really want to learn the material and the lecturers are clearly very knowledgeable, but this course has some clear problems.