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.
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
автор: Benjamin G•
This short course really fills in some gaps in terms of "tricks of the trade"; I think of useful information of this sort as the "force multiplier" whereby some small pieces of advice and insight from a practitioner goes a long way. I checked in a couple of machine leaning books and couldn't find equivalent advice. I particularly liked the point that was made about machine learning and certain ideas becoming obsolete (having previously done a PhD in machine learning) as I had that impression myself and was discussing it with a colleague this very week!
автор: Emily Y•
I like how it discusses everything on a strategic level. Very helpful when leading AI teams in the office. I wish there were a couple more case studies on different AI topics like natural language or signal processing or dialog systems. These are hot topics in the industry and academia and would be helpful to both professionals and students working on these problems to gain some insights to these problems as well. Thank you Andrea and Team! This is wonderful and would high recommend to L&D department to add this to our data science options
автор: Francois T•
0 math, yet I learned so much practical engineering advice, probably more than in all of the theory classes. I am very grateful. I love these kind of classes where we learn a lot from Andrew Ng's practical invaluable experience, and strangely, they end up more difficult than the pure math classes. To me, this kind of classes are higher quality training data for for the NN inside my skull than theory classes of something encapsulated in a library ;). It would take me decades of practice to reach these conclusions. Thank you for the wisdom!
автор: Jairo J P H•
El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!
The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!
автор: Anders S•
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.
автор: Jaime A•
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!
автор: Evandro R d P J•
This is definitely a very important course for those who wish or already work with Deep Learning / Machine Learning. Andrew guide us into debugging and analyzing possible problems and decisions that we, as developers in Deep Learning might come to. The whole course is a very deep analysis on each step during a Deep Learning application, such as Error Analysis, End-to-End Deep Learning, Transfer Learning, Multi-Task Learning and others, it's a complete meal of information that we can absorb. Amazing teachings!
автор: Atul A•
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! 👍
автор: Cédric v B•
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.
автор: Phaneendra R•
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!
автор: Artem D•
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!
автор: vineet s•
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.
автор: Martin K•
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!
автор: Benny P•
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.
автор: Akash M•
This course is quite different from its counterparts. Firstly, this course doesn't teach you the hard and fast rules, that we are accustomed to in traditional computing. This course helps you develop intuitions about measuring the performance and efficiency of our Machine Learning System. This is going to be of extreme importance to all of us. The other courses can tell you how to design a system. This course will tell you how good/bad your system is, and how you can improve it further.
автор: Bruce W•
This was a good course, overall. It covered a lot of the decisions you need to make, when configuring and working to improve your neural network models.
There are not actual flight simulators. That is just how some of the learning exercises are described.
This course made me think about a lot of things--for example, is it better to simulate noise in "clean" data or to try to filter noise out of noisy data. Obviously, this course is just a stepping-off point for your own explorations.
автор: Guy M•
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.
автор: Maximiliano B•
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.
автор: Michail T•
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.
автор: Bhavul G•
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.
автор: GurArpan S D•
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!
автор: MANRAJ S C•
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.
автор: SK A F•
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.
автор: Ryan M•
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!
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!