1 de dez de 2020
I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
30 de mar de 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.
por Vinod S•
19 de nov de 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
por Vinay N•
12 de jul de 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
por Palathingal F•
28 de set de 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.
por Mahnaz A K•
2 de jul de 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.
por Vivek V A•
13 de fev de 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
por Ivan L•
25 de jun de 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.
por Алексей А•
14 de set de 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"
por Rafal S•
22 de jul de 2019
Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.
por Amir R K P•
7 de dez de 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.
por Pete C•
24 de jun de 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.
por Lars R•
29 de ago de 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.
por Andrew R•
30 de abr de 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)
por Poorya F•
10 de dez de 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.
por Hany T•
27 de ago de 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.
por Kody L•
16 de fev de 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.
por Karthikeyan C (•
16 de mar de 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
por Mehran M•
25 de jun de 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.
por Rajesh R•
26 de nov de 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.
por Ross K•
30 de ago de 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
por kArThIk T•
13 de abr de 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.
9 de dez de 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!
por Jussi V•
18 de fev de 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.
por Boris D•
23 de jul de 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.
por Subash P•
23 de out de 2017
There was lot of theory and probably not one of my strengths. However the content is very useful for bringing some structure to machine learning problem solving.
por Jaime R•
20 de nov de 2018
This course could have just been an extra week or two of course 2. It doesn't have the depth of the others, although it is very practical and I like the content