Mar 30, 2020
It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.
Nov 22, 2017
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
By Joshua O•
Oct 19, 2018
Some helpful advice here and there, but a lot of it seemed like common sense. It was not that difficult and a tad boring. Would maybe benefit from having us do actually data collection and cleaning tasks, or implement a ML pipeline and monitoring for the pipeline
By Kj C•
Dec 13, 2017
Generally provides very good advice. Perhaps this course better placed at the end of the course as there isn't much hands-on experience involved and students would benefit form having experience with CNN's and RNN's prior to thinking on project-level scales.
By Jacob T•
Nov 29, 2017
Too many broad statements of "yeah, we generally do this thing for best results" with very little explanation of the background theory. I don't expect advanced math and derivations, but better intuition into why certain best practices exist would be nice.
By Vijay A•
Dec 23, 2019
This course was good, but it was pretty light on content to be considered a separate course by itself. Though the content is valuable, it could've been included as additional/bonus content on either of the first two courses in the DeepLearnign.ai series.
By Tom B•
Apr 13, 2018
I didn't find this course as engaging as Course 1 -- there weren't any coding exercises and it felt like a bit of a let-down after the excitement of coding in Course 1. But it may turn out to have value when trying to start a new AI project from scratch.
By Francesco B•
Oct 6, 2017
This course felt a bit "padded" compared to the previous ones. Also the lack of programming exercises made it seem more theoretical. Finally, the material seems rushed, e.g. there are mistakes in the video editing, strangely long pauses by the teacher.
By Peter G•
Dec 5, 2017
Many helpful insights and advice from an experienced person is always great, but I don't thing this can be qualified as a complete 'course'. As I now see it - Course 2 and 3 of this specialization could easily be merged into one without loosing much.
By Maulik S•
May 31, 2020
The course should have had at least two more quizzes to understand the content better. Also, I would suggest adding programming exercises that help to better explore the ideas of orthogonality, train-dev set correction, and data synthesis.
By Kanghoon Y•
Sep 4, 2019
I got an intuitions from this lectures. But What I want to get from this lecture when I first saw the title, is the method how we can define the activation function at multi-task learning etc. In this video, I got only the overall flows.
By JATIN S•
Aug 27, 2020
This course to me seemed a bit too much theoretical.This could have been a little more assignment weighted so as to bring more focus to study and practise.Overall the case studies were pretty thorough to cover the course material.
By Abhishek S•
May 11, 2020
I think that a lot of this knowledge would have been useful had it been given after building a few projects ourselves (i.e - sample projects), I could not feel connected with the content much and was a little uninteresting for me.
By SHUBHAM G•
Jun 22, 2018
The course must have had some coding exercises showing how wrong the error analysis doesn't work and also some exercises on transfer learning, multi-task learning in order to see in practice how these concepts work in real life.
By Mats B•
Mar 30, 2019
This course did not really feel like a course, just videos and ambiguous quizzes. Some repetition and poor editing of the videos. I recommend to reformat this course to be more substantial and to include programming exercises.
By Мар'ян Л•
Jun 2, 2018
Compare to other courses of the specialization, this has lower quality of video lectures, often repeats things from previous courses and I think it would be better to separate whole course as a separate week of a previous one.
By Gianfrancesco A•
Oct 23, 2017
Very interesting course about guidelines about how to set up a project target oriented, not so trivial. Perhaps an improvement could be to add a chapter on the various DN architectures available for the various tasks.
By Lukas O•
Dec 10, 2017
Would be much better if it included a programming assignment as a final project. I'd like to have a little less scaffolding during the decision-making process to see how well I can do on even more realistic problems.
By Gabriel S M•
Oct 22, 2017
It is a good course because it highlights practical aspects of implementing ML. Some of the test questions were a bit ambiguous though.
I'd also like to have seen Transfer/Multi-task learning implementation exercises.
By Noga M•
Jul 21, 2020
I understand why this course is important, but for me it was the least favorite course so far. Some of the videos were too long and repeat themselves. Maybe it's because I have knowledge in machine learning already.
By Tinsae G A•
Feb 12, 2018
This course is full of intuitions that are very difficult to remember at once. The quiz is very hard and mind teasing. For better confidence, I would like if you add one more case study.
In general the course is good
By Bjorn E•
Sep 9, 2019
Interesting and practical information, but it felt stretched out in an attempt to create a two-week course. With some editing and less repeated information this could be one week that would fit in the prior course.
Jan 31, 2018
Good course to learn about structuring the projects and carrying out error analysis. I wish there were some assignment to work on in addition to the case study quizzes. Assignment really help us learn effectively
By Francisco S•
Oct 25, 2017
The course was just a bunch of tips and suggestions. Yes, they are useful, but given the empirical nature of machine learning I would expect those tips to be accompanied by practical applications and homework.
By Amit P•
Aug 21, 2017
I expected more. The videos were a little long and repetitive. The content was important, though. Maybe the course materials could be squeezed into one week and combined with the previous deep learning course.
By viswajith k•
Jun 24, 2018
THe course was challenging and had valuable inputs. But it would be even more wonderful if we got to work on some portion of the case studies as a capstone project at the very least. Else Its a 5 star course.
By daniele r•
Jul 15, 2019
Good for the numerous hints about practical issues such as different distributions on train/dev/set. Very bad for the lack of hands-on assignments. Good practical advices but no occasion to see them working!