Jan 14, 2020
After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.
Dec 06, 2019
I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse
교육 기관: Alex N•
Sep 12, 2017
Only drawback is that some of the safe checks are wrong in the programming assignments, even with the right seeds.
교육 기관: Khalid A•
Sep 15, 2019
It is definitely very informative, but I wish the lectures would be more in depth in regards to the derivation and proofs.
교육 기관: Ruud K•
Feb 06, 2019
Really love the course, the quizes and programming assignments. But not 5 stars cause the audio quality is extremely poor.
교육 기관: Arkosnato N•
Nov 28, 2017
course content was very good, but this course should be longer. there was a lot of material covered in a very short time.
교육 기관: Michael B•
Mar 17, 2018
More pragmatic approach with theorems would be more appealing....or maybe it is me as i'd prefer Java (DL4J)...not sure
교육 기관: Viviana M•
Oct 29, 2017
I really enjoyed the classes, in the training I would've liked to try and improve the model with all the tools learned
교육 기관: Amit J•
Nov 22, 2019
Great practical insights.
I wish there were programming assignments on "Hyperparameter tuning" and "Batch norm" too.
교육 기관: Christopher S•
Oct 25, 2019
Good intro to the available tools. Very guided course. For concepts to really stick, own projects or courses needed.
교육 기관: George L•
Oct 24, 2018
it's good, but definitely not as good as the first course since Prof. Ng was not very clear on some of the concepts.
교육 기관: Ruixin Y•
Apr 30, 2018
The course itself is great, but the notebook (programming assignment system) is not stable, it's annoying sometimes.
교육 기관: Peter T•
Apr 17, 2018
Useful information, good intuition, but lack of formal results. More homework would improve the learning experience.
교육 기관: Ashutosh P•
Apr 04, 2018
It was a great course. Really well taught by Professor Andrew Ng. Some "from the scratch" coding assignments needed.
교육 기관: Suresh D•
Mar 01, 2018
I hated the tensorflow part though. Would have much preferred it if we could have moved away from jupyter notebooks.
교육 기관: Francisco C•
Jul 24, 2018
Very good content overall. Very well explained and good examples. Many mistakes in the comments in the assignments.
교육 기관: Abhinava K•
Dec 08, 2017
Content is good, but assignments are not interesting. Some application oriented assignments will be be encouraging.
교육 기관: Francesco P•
Feb 26, 2019
I would like to see more programming assignments. They are very well done and it'd be great to have more of those.
교육 기관: Angad P S•
Dec 13, 2017
I would really benefit from this course if more assignments are provided to try different data sets and scenarios.
교육 기관: Giovanni C•
Feb 11, 2019
I liked the course, but the explanation of tensorflow needs more propaedeutic introduction for a learner like me.
교육 기관: Charbel J E K•
Jan 17, 2018
Really helpful ! Too much concepts to understand but only applying few in the course. I really liked this course.
교육 기관: Jay R•
Dec 24, 2017
Good course to get familiar with hyperparameters and improving the neural networks. And cliff hanger was amazing!
교육 기관: Mads E H•
Oct 26, 2017
Nice and practical. The assignments could go a step further in trying out different things to get better results.
교육 기관: Jayanthi A•
Apr 05, 2018
It was great course, however, I would have liked it to be a lot slower with more time being spent on Tensorflow.
교육 기관: Joshua S•
Nov 13, 2019
A good course that provided more intuition on which models to work with and how to tune parameters effectively.
교육 기관: Aayush A•
Aug 03, 2019
The Jupyter notebooks had a lot of mistakes which wasted a lot of my time otherwise the course content was good
교육 기관: Corbin C•
May 10, 2018
Good lectures, but the jupyter notebook examples are inconsistent and sometimes use deprecated Tensorflow code.