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.
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.
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By Felix E•
This is a 2-week follow-up on the previous two courses in this specialization.
While it's a decent course that goes over a few interesting topics, I have a hard time giving it more than three stars. Reasons for that are below:
(1) Especially the first week felt very slow and repetitive. Most of the material could have been summarized a much smaller timeframe.
(2) The course went over some interesting topics in a very high-level way, but skipped a lot of the details that would have been very interesting to people looking to learn deep learning in depth (like the target audience of this course!).
(3) While I think the approach of having some themed case studies for the test is neat, a lot of the answers left me thinking "well, the correct answer would also depend on X which isn't specified". Good concept to test knowledge in a "discussion/oral exam" session, but IMHO bad for hard "wrong or right" multiple choice tests.
(4) Some videos had "black screen" times at the end, errors, cut-offs and repetitions were not cut out, and overall I think this had the least amount of "polishing" of the courses in this specialization so far.
I'd have preferred if the content of this course were a bit more steamlined and merged it into the other courses of this specialization.
By Aristotelis-Angelos P•
Overall, I think that it was a good course but in my opinion, the knowledge of this course cannot be easily transferred to people with very few experience in Machine Learning. Therefore, I was wondering whether it should be the 3rd course or the 5th course in this Deep Learning Specialization! Moreover, in order for someone to deeply comprehend these concepts such that he/she is able to apply them in a Machine Learning project, he/she should work on a project on his own where he/she will meet these concepts and will have to search in order to solve them.Last, personally, even though I am quite satisfied from the courses, I would expect that one more course is added to Coursera which is going to require to build a Deep Learning project! I think that this course should be of more advanced level and require (not Intermediate as those ones) and should require from students to build projects like the ones builded in the cs230 class from Stanford.Greetings from a PhD USC student
By Todd J•
The content in this course is excellent; however, the learning activities are insufficient for truly internalizing the material and do not follow evidence-based guidelines for learning (see the book, Make it Stick). The video lectures cover a lot of ground, but I found that many were a bit too long, often dwelling on points well after they were made. The problem is that the only actual learning activity is a 15 question multiple choice at the end of each week (and there are only two weeks of material). The course would really benefit from having questions embedded in the videos, similar to Udacity style courses. Following those with the 15 question "simulator" would then reinforce the material. However, this course also needs programming assignments at the end of each week so that students can actually gain real experience with the methods and suggestions.
By Anne R•
Good general information is provided but this material could be layered into the other courses in this specialization. I would recommend that the case studies be based on real industry problems that present the backstory of the decisions the teams made. Also programming assignments would be useful in which the impact of incorrectly classified training data is studied in detail or in which images that have been synthesized are used versus not used. It did not take too much time to work through this course so the information provided is worth the cost, but I am not convinced that this series is viewed as more than an opportunity to make some money off of the name brand. Much of the information provided so far is covered in the Deep Learning - Goodfellow text and the extras are vague and repetitive.
By Jonathan C•
There's some very good tips in this course, but it's not enough content to even warrant the two weeks that's it's spread out over. It would have been high quality material as a part of another course or as an addendum, but it hardly stands alone by itself. Unfortunately, that seems to be the trend on Coursera to provide sparse content spread out over multiple courses to milk money. Entire specializations could be fit in one course but now it's 7. Viewed in context of this specialization, a lot of what Andrew Ng lectures on seems to be padding to lengthen the videos since he tends to repeat the same points over and over and over. In other words, a lot of what Andrew Ng lectures on seems to be padding to lengthen the videos ... see what I did there? Maybe I can get a job at Coursera?
By Laurence G•
Some interesting information in week 2 where multitask learning, transfer learning and end-to-end vs sequential nets are discussed. The bit on breaking down your errors into classes will also come in handy!
Week 1 was quite repetitive and seemed to be mostly common sense, probably could have cut these videos in half without losing much. Personally I watched most of this at 1.5x speed to avoid falling asleep. First quiz also had some less then conclusive answers - there's a lot of disagreement in the forums! Some issues also with the cutting of the videos, those these are only a minor nuisance overall.
Overall, less impressive then the other courses but still useful knowledge can be obtained here.
From a philosophical standpoint, I especially liked the 2 interviews in this course.
A huge decline comparing to the absolutely amazing precedents. Though the content is important and relevant, it is designed mainly for actual practitioners in the field, which is a mismatch with the audience of the specialization. The lecture is repetitive to the extent that I doubt I hit backwards by mistake. The video is raw with vocal tests and black-frames uncut, minutes of vacant content. The quizzes are trivial and not enough to really solidify your understanding comparing to the perfect programming assignments before. If out of the context of the whole series, I would give it 1 star. The quality of the specialization is great, but to pack such little content into a "course" is disappointing.
By Srikanth C•
This course offers some good advice when it comes to (much needed) practical considerations when training neural networks, and to a reasonable extent machine learning algorithms in general. I personally don't see myself successfully applying the content on Multitask Learning, Transfer Learning and End-to-End ML in real scenarios straight after finishing this course unless I go ahead and learn these in more depth. The "flight simulator" approach to applying what has been taught was great! I would have liked more (perhaps optional) exercises in this format. I would have also not minded a longer course that could go more in depth into the bias-variance tradeoff and the aforementioned topics.
By Xiaoming W•
This course is too high level and short - while the content and concepts themselves as presented were invaluable, they were insufficient to give a good overview of what a basic machine learning project structure needs to contain.
It would be much more helpful if programming exercises were provided which give an indication of *good code architecture* when it comes to structuring a machine learning project. How do we write reusable functions/classes which split, process, and combine train/dev/test data, feed them into a learning algorithm, and carry out the necessary error analysis?
By Xizewen H•
Great materials but 1) quiz questions are sometimes vaguely stated thus causes confusion, while almost no one from the course stuff is giving satisfying answers in the forum to help clarify; 2) multiple mistakes in video editing, e.g. part of clips played repeatedly, and blank dark background without any content somehow got inserted into the video; 3) really hope to see another programming assignment in Tensorflow; not that I don't agree with pilot-training assignment, but programming would be good to have because essentially this is where data science projects are built.
By Apolo T A B•
Not exactly what the title promises. In this course you will learn more about the overall approach of a ML than how to organize your data and best practices on comunicating and sharing information. (at least in week one, so far haven't started week 2).
Now I've done week 2, is much better than week 1, but still the problems presented are way more in a way of the rational behind the ML projects than Structuring the project itself, peharps a better title would be: "DEFINING GOOD MACHINE LEARNING STRATEGY APPROACHES" or something like it.