Mar 31, 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 02, 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 Anders S•
Dec 24, 2017
Best applied course I have taken so far. Very practical, great to do before starting a project. I do have a suggestion for the specialisation in general. I have been working with deep learning, specifically image recognition and I have had a hard time figuring out what images I need to feed into my algorithms. Material about what type of data is needed to train algorithms correctly and overall requirements of this data would be great. I know this is done in some level, but not in a level of detail necessary for a project.
By Jaime A•
Sep 08, 2017
Probably the best course to learn how to approach a Machine Learning project and deal with all the multiple challenges and issues which arise in real applications. Lots of years and experience of ML work distilled in a set of practical recommendations which can save one and entire teams months of work and computing expenses. The quizzes, based on simulated real cases, help mastering the recommendations. An ideal course for the more novice practitioners to catch up with the most expert ones in just a couple of weeks!
By Atul A•
Aug 24, 2017
Great course! This is the first course I've seen that gives a "big picture" overview on *how to approach* new machine learning / deep learning projects. It dives into how to structure the project, how to separate training / validation / test datasets, how to perform error analysis when your errors are high, how to trade-off bias/variance, and when and how to apply end-to-end deep learning. In short, this course is about finding the right trails, rather than going deep in the forest. Highly recommended! 👍
By Cédric v B•
Aug 14, 2019
This course contains some very essential information regarding the appliance of machine learning in a project. I think that it really discerns itself in this regard when compared to other courses. The lectures are very clear and I particularly enjoyed both exercises: the questions were very well chosen. Also, I quite like the 'Heroes' videos (also in the previous courses) as they also provide some very good information on the field of AI / ML in general as well as some practical tips on how to enter it.
By Phaneendra R•
Jul 03, 2019
One of the best courses I have ever gone through, the lessons were short and to the point thus allowing me to absorb the concepts even though they were bit outside my experience. Andrew generalized the topics so effectively that I could relate similar experience to understand the concepts. I love Andrew's simplistic, repetitive, regressive approach so if things aren't clear in the first go, you can trust him to reviw them at the right opportunity. I would love to learn more on this topic from Andrew!
By Artem D•
May 29, 2019
This is a very interesting course with very useful recommendations which could be also applied to ML projects. I highly recommend this material.
The only downside is that the course is structured as 1-1.5 hours of lectures and then practice quizzes (which are actually very interesting). And as for me, it becomes boring just listening without hands-on then, say, 15 minutes, despite the material itself is very interesting.
I hope that the next courses will have more practice.
All-in-all, a very good course!
By vineet s•
Apr 26, 2020
Very important course. Most of the stuff in this course is what is important for practitioners and is missing in other courses, I think most of the organizations and teams miss out on the strategy and devote far more time in wandering in wrong directions. It would be helpful if at certain points when referring to some concepts, a brief recap be given. There are too many concepts in other courses that at times you have strain yourself to recall. It is good mental exercise but it may help some folks.
By Martin K•
Jan 15, 2019
This course completely wrapping up the topics from course 1 and course 2 of the deep learning specialization while presenting up-to-date (and fun(!)) "real" word evidence cases. From all the courses in the specialization, I found this one particularly compelling in terms of easy-to-grasp and the best overview of ML projects. The assignments were outstanding, making you really the feel like you truly understand ML challenges, use cases and solutions to problems.
Totally recommend this course!
By Benny P•
Feb 24, 2018
This is a very good course on machine learning subjects that are rarely discussed elsewhere, namely managing machine learning project. And surprisingly, despite the easy feel of the subjects and their explanation in the video, the decision making that you have to take (and is tested in the quiz) in simulated project is hard. As project leader, given many choices of things to do, it's hard to decide what's the best thing to do, and this course shows, teaches, and trains you how to do that.
By Guy M•
Sep 05, 2018
This course felt a bit out of sequence in that it left behind the more "hands on" notebook coding for a higher level "How to manage an AI team/project". This made sense when I realised it used to be the last of a three-course specialization. Aside from how it fits into the flow of the specialization (which then moves on to get technical again with CNNs and RNNs), it's jam packed full of incredibly sound advice that even experienced team leads would probably benefit from reviewing.
By Maximiliano B•
Jan 02, 2020
In this module professor Andrew NG teaches several strategies based on his vast experience to help you deal with real world machine learning projects. Most of the information is of great value and it is difficult to find organized like that in another website. I have really enjoyed the two case studies proposed and they are very interesting to help you review the concepts studied. Finally, professor Andrew NG explains the content clearly and it is a pleasure to watch his videos.
By Michail T•
Sep 04, 2018
This is another awesome course teached by the best instuctor (prof.) in the net for ML and DL technologies. Knowing how to divide the dataset in the appropriate sub sets and doing the right error analyisis, is the main goal every developer or scientist in this field tries to achieve. This course teaches all this and additional concepts like transfer and multi-task learning which are essential techniques to improve productivity. I would give six stars if there were any.
By Bhavul G•
Apr 22, 2018
I feel humbled as I ended this course, realising that years and years of knowledge that Prof. Andrew and others have gathered they've just let out to public, accessible to everyone. It is such a great act of kindness. I am really thankful to you folks. This was a great course to learn the insights of an experienced ML / DL guy. It would help a lot when I'll actually be working on a real life project. I hope I would be able to spread the light of knowledge even further.
By GurArpan S D•
Oct 21, 2017
This may be an optional course in the deep learning specialization, but I beg to differ. If you plan to do actually start a project in machine learning, it is imperative you take this course. You could finish your project ten times faster with a fraction of the work. All in all, every one of these courses in this specialization have been beautifully organized and taught very well.
Thank you so much for offering this course along with the others in the specialization!
By MANRAJ S C•
Oct 27, 2019
The course is really great! It offers an in-depth understanding of the practical aspects and applications where deep learning can be applied. Most importantly the content in this course will help you iterate faster with your machine learning problem by doing error analysis on it. This course tells you exactly what can be done in which situation to improve the performance by analyzing the data and other statistical aspects of the data as well as the algorithm.
By SK A F•
Jan 10, 2020
Error analysis and Learning the methodology to handle the errors. Besides the traditional systematic way of performance analysis like train-dev-test and cross-validation, Andrew focused on data mismatch and train-dev data. These two are the most important things that are described very well. Like other courses, Andrew was very good to describe the real-life practice. In this course, two simulation quiz really helps a lot to deeply understand the application.
By Ryan M•
Sep 14, 2017
This is a VERY valuable course, with tons of practical advice on how to understand problems all machine learning / neural network developers experience and how to tackle them. I have never seen such high quality practical advice in any textbook or in any other course before, and I believe that even those who are not taking the full five course deep learning specialization should seriously considered this course. Another truly excellent Andrew Ng course!
May 01, 2018
This course introduces some general principles for developing a deep learning project. It points out the difference of setting of train/dev/test sets between deep learning and traditional machine learning. That's a practical advice. And it's notable to include human performance and regard it as Bayesian bound, almost the best we would expect an algorithm to achieve. That saves you from spending unnecessary time to make a subtle improvement. Learnt a lot!
By Stanley C D•
Sep 08, 2018
In this course I learned about ways to approach some of the real world challenges that I have already faced on some of my own projects. For example, what actions should you consider when you find a significant number of labeling errors in the dev/test sets that affects your ROC. I also was motivated by the last module on end to end training and the interview with Ruslan Salakhutdinov to pursue an end to end training idea that I have been thinking about.
Nov 02, 2018
Great Course! Many students will choose to skip this course since they think there are less knowledges than other course in the 5-course specialization. But I have to tell you: this is the best course in the specialization, because you can learn a lot knowledges especially skills and experiences in practice from this course that you can't learn from other books, courses or universities. BTW, I'm not telling that the other 4 courses are not important.
By Daniel S•
Dec 17, 2017
Andrew Ng is brilliant! I have never seen such a great tutor in my life. He bring extremely useful concepts and explains them so easily in a way the concepts stay in your mind.
Like the backprop algorithms he talks, he has learned so much from his old course and he has made great improvements to focus on New people. He sure has a good deep network up his brain that has gone through lot of iterations (without overfitting) with beautiful set of features.
By Kryštof C•
Nov 07, 2018
It is very good probe to practice. I would very appreciate to take this course before I have started in machine learning. It would help me to realize some mistakes I have maid before. On the other hand, for people, who have some experience with machine learning, some chapters are being over-explained, as the topics are quite clear to those people. Overall: I would recommend this course to everyone, who wants to start with his/her own NN training.
By Teguh H•
Nov 29, 2017
No coding at all. But this is one of the best course on AI, because it does not talk about coding or anything, but most importantly, the one thing that is not taught by many others. Experience of Andrew Ng trials and errors in approaching ML projects. How to create structure, how to observe what results to see. In short this course is like 'how to save time in doing AI projects and make optimal use of it, avoid trial error which can cost months.'
By Luis C G•
Oct 19, 2017
Despite of its relative simplicity (from a technical point of view), it is probably one of the most practical courses I have taken in Coursera. Even though it only mentions deep learning, the overall methodology can be applied to any machine learning work. It is important to get familiar with the heart of the models, but it is probably even more important how to work on an end-to-end machine learning project. In summary: Highly recommendable!
By Danilo Đ•
Jan 04, 2018
Unlike most of the Deep Learning knowledge which can be found in literature and other MOOCs, this course provides you with insights that can only be acquired trough (often painful) trial and error. Here you learn how to approach Deep Learning projects, how to avoid most common mistakes, and how to quickly identify errors in your model.
Do yourselves a favor, and finish this course before taking on your very first DL project.