MG
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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.
NI
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Awesome course as always. The course teaches real world practical aspects of how to get started and navigate in the real world projects. The guidelines are actual learnings from years of experience.
By Jean M A S
•Oct 27, 2017
The simulations were very good to build a good intuition about setting up a machine learning project.
But I regret that we didn't have coding exercises. 4 stars for this one.
By Carlos S C V
•Apr 15, 2020
Me gustó el curso, pero creo que algunas lecciones fueron un poco más largas de lo necesario. Debo agregar que me gustaron mucho los simuladores, creo que me ayudaron mucho
By Vinod S
•Nov 19, 2017
Helps clearly in understanding practical aspects of deep learning. An additional week, highlighting the aspects of productionizing a deep learning project would have helped
By Vinay N
•Jul 12, 2020
Since I myself am working on a few projects, the concepts here are somewhat useful in error reduction. Especially when the models are used to automate medical applications
By Palathingal F
•Sep 28, 2017
A unique course to understand the process of establishing a ML project. But lacks tools information and a more structured definition of the process. A bit too theoretical.
By Mahnaz A K
•Jul 2, 2019
Thanks for the practical tips and insights from real projects.
Your pool of heroes of deep learning is very skewed. If the field is so skewed, then it's a bigger problem.
By Vivek V A
•Feb 13, 2019
Good course for the ones who already started developing ML systems. This will help us in improving the ML systems and identify what can be done for which kind of problems
By Ivan L
•Jun 25, 2019
Most of the material was quite useful, but some was, perhaps, too obvious. Also, some things were discussed too thoroughly, and, in my opinion, that was a waste of time.
By Алексей А
•Sep 14, 2017
Would be great to obtain more concrete information.
For example, instead of "requires much more training data" to obtain "requires ~1'000'000 samples instead of ~100'000"
By Rafal S
•Jul 22, 2019
Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.
By Amir R K P
•Dec 7, 2018
I wish there was more examples, visualization and depiction of work with referral to papers or experiment here. or perhaps a bit of project management, data management.
By Pete C
•Jun 24, 2018
Enjoyable, but felt a little less challenging and more hastily assembled. Regardless, the material is valuable and as always, a pleasure to be instructed by Andrew Ng.
By Lars R
•Aug 29, 2017
The course material is relevant and useful, however, I agree with other reviewers that these 2 weeks should rather be a 1-2 weeks addition to one of the other courses.
By Andrew R
•Apr 30, 2018
Quick course. Worth taking because gives some practical guidance on what avenues to pursue when finding a optimal model (which takes into account human time required)
By Poorya F
•Dec 10, 2017
The first week is too long with repetitive materials. The second week is very interesting. However, I wish the course was designed such that it required some coding.
By Hany T
•Aug 27, 2019
Great course, great professor .. the only issue is that I feel sleepy every time I watch the videos :), it's some how single tone. Also the audio could be improved.
By Kody L
•Feb 16, 2022
Not quite as practical and informative as the first two courses in this specialization, but overall still quite enjoyable and helpful. Excited for the next course.
By Karthikeyan C (
•Mar 16, 2020
It is always important to learn above the problem-solving methods and tools. This course teaches the complete diagnosis methodologies for Machine Learning problems
By Mehran M
•Jun 25, 2018
Overall, very informative, however I think the content of this course could be divided between the first and the second course.More assignments would've been nice.
By Rajesh R
•Nov 26, 2017
Lots of practical advice and ideas on how to work on actual projects and things to look out for. Great stuff. Wish it had a few programming exercises or a project.
By Ross K
•Aug 30, 2017
Useful introduction to meta-level principles of machine learning process management, but not quite as groundbreaking or well-instructed as the previous two courses
By kArThIk T
•Apr 13, 2020
A real time project or programming assignment could improve our confidence level.
All of these courses if it had readable material along with video, it'd be great.
By SYZ
•Dec 9, 2018
Hope to have coding practices for the second week's materials.
Anyway, the current course is already very helpful. Thanks to Andrew and all staff behind the scene!
By Jussi V
•Feb 18, 2018
Content is good, but a bit thin... This course makes sense as part of the deep learning specialisation, even if this is a bit too short to be a course of its own.
By Boris D
•Jul 23, 2019
A bit less interesting than the others I think. To me the whole first week was full of obvious stuff. The second week, however, was very interesting and helpful.