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
Dec 24, 2017
Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.
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 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 Morisetty V A S K•
Jan 20, 2019
Interface for evaluating is not great and assignments are easy
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 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.
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.
Mar 21, 2019
By Salim S I•
Aug 12, 2018
Would have liked programming assignment in python to understand the various initializations and optimizations. Although tensorflow introduction was good, It felt like being left stranded without a python assignment to cement the things learnt in the class.
By Ashvin L•
Aug 25, 2018
The course builds up on the first course and provides some ideas on how to tune the networks to perform better. However, at the core, I find the number of parameters overwhelming and it appears that by changing the parameters we can get any answer we want. There is no "formal" and mathematical basis for changing the parameters. This is a bit disconcerting.
The assignments were trivial. More importantly, at least one assignment appeared to indicate that the results are entirely dependent on weights chosen (at random) on the first iteration. This should not be the case.
By Peiyu H•
Oct 12, 2018
Lots of error on the final exercise. It seems some errors exist from previous sessions already. Hope the teaching team will fix the errors and make learning less confusing for us.
By Jérôme C•
Oct 14, 2018
Need more training on Tensorflow, imho
Sep 18, 2018
By K K R•
Sep 17, 2018
Some of the videos are very abstract and needs a bit of mathematical intuitions. These intuitions are best obtained by calculations rather than a lecture :)
By Gadiel S•
Sep 21, 2018
The course is good. It covers important ideas, and they are well explained in the videos. However, the formulation of the assignments is sloppy. There are mistakes and inconsistencies, in some cases necessary explanations are missing, and in some cases the instructions are misleading (I suspect the assignment has changed over time, but the instructions have not been consistently updated).
By William K•
Oct 01, 2018
I thought the content was well-chosen and typically presented clearly. However, unlike the previous course in this specialization, the assignments had an egregious number of typos and missing information. I found these errors confusing and time-consuming.
From the staff's forum activity, it looks like they are no longer actively involved in this course. I hope that Coursera will hire someone—an intern would probably be plenty capable—to take this course and carefully fix as many of the errors in it as she or he can find.
By Ha S C•
Oct 29, 2018
A much sloppier and poorer course than previously. Grading mishaps (on the fault of the grader), a few errors in the lectures (the variance in the normalization), and very basic and unhelpful feedback from staff made for a course that did not live up to the level of the previous one. If at any point you need further help, it is generally unavailable, or difficult to find at best.
By Imad M•
Nov 04, 2018
Week 1 and week 2 needs more examples of python programming in the videos. The videos for week 3 were a lot more interesting. Without the python implementation examples in the videos, the course can be very dry.
By CARLOS G G•
Jul 14, 2018
By Juan J D•
Sep 11, 2017
tensorflow subject was to superficial
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.
May 06, 2018
ike usual andrew ng perfect explanation simple go to essential stuff.
the minus points some troubles with notebook
big thanks for andrew ng's team.