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
Oct 09, 2019
I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation
by Noah M•
Dec 10, 2019
With the basic knowledge I earned in course 1, it was very helpful attenting this coruse on improving Deep NN and I took a lot of notes during the course, to which can refer in the future.
by Vikash C•
Jan 28, 2019
Content was good.
But the system that checks our submitted our code checks wrongly even when I wrote it correctly.
In week 2 assignment, when I submitted the code, it gave many functions as wrong coded.
I resubmitted the code after few changes, for instance a+= 2 changes to a = a+2 and string text like 'W' changes to "W". It worked fine and gave 100 points.
In short, what I observed is that the code checking system is taking a+=2 and a=a+2 as differently, also 'W' and "W" are considered different, but they are not in actual output.
by Morisetty V A S K•
Jan 20, 2019
Interface for evaluating is not great and assignments are easy
Feb 01, 2019
week 3 topics would have been a bit better
by Amit C•
Feb 01, 2019
I wish the course mentors were more active on this course makes it a bit difficult to clear doubts
by srinivasa a•
Jan 09, 2019
its great foundational course but i feel with frameworks available the math behind it was little boring.Andrew NG is pretty good with explaining it well but sometimes felt it was too trivial
by Long H N•
Feb 13, 2019
Mar 03, 2019
by Jorge G V•
Mar 07, 2019
The lessons are good, the programming assignment has mistakes that have apparently been reported over a year ago and have yet to be fixed - there is no excuse for this to be the case.
by Till R•
Mar 02, 2019
Exercises are too easy, and lectures are kind of boring. The Jupyter / iPython system does not run smoothly. I ended up downloading everything on my local computer, completing the assignment there, and then pasting the code into the coursera notebook. That makes the assignments take 50% longer than necessary.
Mar 21, 2019
by Tan K L•
Jan 26, 2019
I think more should be done regarding the TensorFlow framework with more explanations given to what the functions did
by CARLOS G G•
Jul 14, 2018
by Navaneethan S•
Sep 20, 2017
This course was much less rigorous and theoretically-grounded than the first. There didn't seem to be much justification for any of the techniques presented, which was a stark contrast to the first course.
However, the topics are important and useful to know, so I'm glad they were covered. To me, the most useful sections were on softmax regression and deep learning frameworks, which I really enjoyed. The TensorFlow assignment was also interesting and (relative to the others) challenging.
I think there is a lot of scope for this course to be improved and I hope Dr Ng and team will do so in the near future.
by Nikolay B•
Dec 05, 2017
Lessons are nicely explained
Assignments should be more challenging. Same as first course, this one basically make you cope-paste instructor notes and just change variable names to pass all assignments.
by Todd J•
Aug 18, 2017
Very mixed feelings about this course. The course title and nearly all (but 20 minutes) of the video content are on the topic of hyperparameter tuning, regularization and optimization of neural nets. This material is excellent. However, the programming assignment for Week 3 is about building a simple model in Tensorflow, with no coverage the rest of the material from the week. It is as if they included the wrong assignment, or just forgot to include the appropriate assignments to practice the actual content of the course. In addition, the Tensorflow intro in the videos and the Tensorflow assignment are not that great an introduction to the concepts behind Tensorflow. There are much better tutorials available on the web, such as from Tensorflow.org and codelabs.developers.google.com
by Minglei X•
Oct 22, 2017
Some process that was discussed in details in previous courses are mostly omitted in new context. While it is sometimes nice for saving time and focusing on new ideas, I feel like there are sometimes subtleties in them. Like I could not imagine how backward propagation should be implemented in batch norm. I'm not sure if it's because there are really some subtleties that you think it's too tedious and not necessary to introduce in the short video. If it is the case, I still hope you could provide more detailed information about them somewhere, just for curious people like me.
by HAMM,CHRISTOPHER A•
Apr 30, 2018
Lots of theory and not enough practical implementation.
by QUINTANA-AMATE, S•
Mar 11, 2018
Again, nice videos but not
by Dimitrios G•
Nov 28, 2017
The course continues on the same path the previous Deep Learning course has set but I found the use of TensorFlow somewhat limiting. It is a great tool that simplifies the training and running of NNs but it does not allow for easy debugging or for easy looking within the built-in functions to spot problems. I felt that we were treating many tf.functions as black boxes and I am not so fond of this. Otherwise the course was fairly useful.
by Amod J•
Mar 18, 2018
Want to download my own work but cannot.
by Younes A•
Dec 07, 2017
Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him.
by Tushar B•
Jun 12, 2018
Assignments vs lecture, difference is huge
by FREDERIC T•
May 13, 2018
Good courses, the sound quality is very poor (high tone noise).
Mar 14, 2018