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.
Jul 1, 2020
While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).
By Matthieu D•
May 13, 2018
I'm grading this course lower than I graded the two previous ones for two reasons: 1) while there are many examples given in the course, it is actually hard to take a step back and see how to concretely achieve some goals in a more generic manner, and 2) in the assignments (which are made of quizzes), many "wrong" answers would actually be appropriate if more context was given.
By Nathan W•
Feb 19, 2021
This course really felt a lot more thrown together than the other ones, with a less cohesive lesson and quizzes that had more subjective material in them than usual. And perhaps it is a bit nitpicky, but I found the swipe Ng took at computational linguists to be kinda distasteful. I know there is a lot of bad blood between ML and AI people, but it has no place in coursework.
By Reza S•
Feb 15, 2020
Thanks Andrew for this course! However, it is obvious that less care was taken for the preparation of this course compared to previous courses (more typos, etc). Some of the sentences in the quiz were not clear at all and made it very confusing to choose from the options. A little programming assignment at least would be nice to reinforce our learning of the materials.
By Gustaf B•
May 10, 2021
The course goes through valuable practices when it comes to analyzing errors, and Andrew does a great job at explaining. Though, I felt that there should have been programming assignments to accompany the theory. I strongly prefer the layout of previous courses, "quiz -> programming" as that feels more interactive than just doing a quiz in the end.
By Jason C•
Dec 26, 2017
nice lectures and very useful knowledge learned by Andrew, but it is really short and no working assignment through real code.... and quite a lot more mistake than course1 and 2. Really love the two previous courses, don't work why the quality of the course drop off so sharply.
Somewhat disappointed, but still really great lectures.
Aug 28, 2020
It gave much more industry driven approaches to improving the model. I as a student don't have that much experience with deeplearning and that' why I couldn't relate with most of the topics that were going on here. Of course, the teaching quality was supreme. But the course's contents itself felt a little bit dry to me.
By Sagar B•
Oct 29, 2017
The course work is really good. It has a practical emphasis. However, I did not like the quizzes (especially week 2 quiz) in the sense that the options are not very clear to understand and you end up being more confused. I hope the team works on the clarity of options for people who take it in future.
By Fabian A R G•
Oct 28, 2017
Even though the materials in the course are very interesting, I would expect that in the third course we would have more tools in order to work by ourselves in a project... It would have been amazing a final project where you can put together this tools. Nevertheless it is still an interesting course.
By David B•
Oct 6, 2017
This course was less satisfying then the 2 previous in the specialization. A lot of repetitions, no programming exercices. Interesting test cases but feels a little out of scope because we have not done image and speech reccon yet. Consider putting the course at the end of the specialization maybe?
Mar 26, 2019
I think the week 1 was overstreched. There was not much content to deliver and for the first time Andrew's classes made me sleep. It was like the boring lectures we get at school. I think we can easily shorten the length of this course or just scrape it and add it to course 2.
By Andrej P•
Jan 26, 2018
I found this course to be a bit confusing with regards to what data set (training/dev/test) to fix under what conditions and so on. I've also missed having a practical home work, the case studies were fine, but I find that practical applications help me remember things better.
By Filip R•
Mar 18, 2020
Some of the quiz questions (especially in the first week) were quite ambiguous. If I did not take the quiz directly after the videos, I don't believe I would be able to pass, Also some written summaries as in the 1st Ng's Machine Learning course would be helpful.
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.