Nov 23, 2017
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.
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.
por Nkululeko N•
Apr 15, 2020
Failure on a beginner level quizzes it's very irritating, more specially to me. I don't regret seen myself have to re-do some quizzes for every week, probably that's because English is not my first language, or how can I put it...my mother's tongue. I believe it's an indication of our weaknesses and if we face them we can grow to prosper, not that I am trying to be a life philosopher. Questions on this course are made in such a way to test if you really understood what the instructor has taught you. I love Andrew's ways of teaching, I just wish he was my electronics lecturer. I feel like I could have understood some of fuzzy concepts that I battled with very easily. The concept were given in such a structured way and I was very excited in many of these teaching and insights regarding machine learning approaches as a machine learning engineer.
por David M•
Sep 01, 2017
This course is radically different from the first two of the specialization. While before we were dealing with the theoretical basis for how learning works and ways to optimize the performance of the computer, this one is more like a stream of tips, cautionary tales, and hacks in order to optimize the performance of the human. Personally, I found the material to be very educational and engaging, with many "aha" moments when the instructor makes you see the "obvious" solution for a problem that just seconds ago seemed unsolvable.
The assignments (the "flight simulator") are incredibly useful and make you think profoundly and systematically on the problems. I found that the questions would typically prompt even more questions in my head and make me consider many options to tackle a particular problem.
por XiaoLong L•
Aug 15, 2017
After seven days learning, I finally finished the three course of this specilization. I've gotten much more than I've expected at the beginning. Not only deeply understand how the neural network works, but also how to build deep neural network and how to train it efficiently. Now I know how to start to build a machine learning project and solve the specific problems from data preparation to model training and I know how to quickly get my network works through transfer learning and fine-tuning, etc. By watching the interview videos I got a lot about the future of AI and I deeply know what I am really interested in now. I really appreciate what Prof. Andrew and TAs have done to make this series available from all around the world and I really too impatient to wait to learn the next two course.
por samson s•
Dec 09, 2017
This is probably the most important course in the specialization. It's very easy now-a-days to create Neural Networks and get a grasp of how they work due to high-level frameworks (keras, scikit, tflearn, etc) and abundance of literature and videos, respectively. The thing that is lacking from most resources that I have encountered on learning Deep Learning and Neural Nets is how to optimize and approach problems. I have in the past build some complex Neural Networks, but would hit road blocks that would ruin productivity for I didn't know how to approach problems correctly, and didn't know what knobs to turn to improve performance of my program. This course teaches techniques that I find extremely useful for my previous problems in Machine Learning.
por Louis-Marius G•
Oct 20, 2017
Very useful knowledge, super interesting material and prof. Ng is an awesome teacher as always. The simulating approach for the quiz is great! However the "simulation" questions and answers should be carefully reviewed. Sometimes the "right" answer is difficult to choose because of an ambiguity or a little detail that does not quite match the lectures and two answers seem to have some of the right element OR no answer seems to be perfectly right. Going thru the forums, you will find plenty of comments like this to figure out which questions to tune. Some are right and some are due to the student genuinely making a mistake. Perhaps looking at the error rate on each question will also help seeing which one are abnormally incorrectly answered.
por Michael K•
Aug 14, 2017
Loved the course because the insights shared by Andrew Ng are clearly coming from real-world industry experience. Besides the content of the video lectures, which are a must-see for every ML practitioner, I particularly liked the "flight simulator"-style assignments.
Although the content is of very high quality, I noted that there a couple of mistakes in the assignment texts, unfortunately sometimes even in the options of multiple-choice questions, which make it unnecessarily hard to guess what the option actually means. In one case (assignment 2, question 10) I even think the "correct" answer's text is contradictory to what Andrew says in the lecture. I feel that half an hour of proof-reading could have taken care of these mistakes.
por Francis S•
Aug 26, 2019
Previously, I have taken online classes before in Machine Learning by going the cheap route (Udemy, blogs, youtube) and you get what you pay for. Andrew Ng explains it the most thorough, easiest, and simplest way possible. Presentation material is very understandable. Great class for new machine learning learners. Highly recommend it. The only downside is that the programming exercises are little too easy in my opinion. I feel like the best way to get your hands dirty is to do actual projects (do your own projects). These lectures are good for intuition and background of different types of Neural Network architectures. Other than that, Great material. Thanks Andrew!
por Chou C C•
Dec 10, 2017
In this course, I learned a lot about how to make right decisions when facing different problems in machine learning tasks. It helps me to review the decisions I made in the past, and also shows me a more systematic way to think about what to do next. I strongly recommend everyone interested in ML to take this course.
The only thing I'm not so satisfied with is that some questions in the quiz are quite confusing. Maybe they just have wording issue, but these questions and their corresponding answers do confuse a lot of people. I think maybe TA could take some time to address these problems in the discussion forum and help us learn even better.
por Ernest S•
Oct 29, 2017
Another excellent course made by Andrew Ng. It is another perfect example of how to prepare good learning materials.
This course does not in fact expect you to write the code. Teacher is aiming not to offer you his abilities to make working system. He is offering you his deep insight and experience in making systems better and better to the point in which they meet expectactions. He discusses how to address issues you may encounter in systematic manner and where put your resources to use them in most efficient way.
If you are building machine learning models I am sure that this course pays off and can spare you many mistakes you could make.
por José A•
Nov 06, 2017
This is a passive course. Don't let the 2-week course set you off. The videos in here are really insightful. They give you some of the experience that Andrew has seen throughout the years.
They will provide you with the right way on how to split the data sets, how to handle when the train, dev & test sets come from different distributions; advantages of orthogonalization; The avoidable bias, the satisfying and optimizing metrics.
By investing in this course, this will save you tons and tons of hours of work by understanding some key concepts that you will need for an effective Machine Learning problem.
por Ali A A•
Sep 25, 2018
An amazing course indeed. A bit "dull" to some due to the lack of programming assignments, but extremely beneficial and insightful to anyone seriously considering to tackle an ML project. You have to appreciate the fact that while what this course covers may sometimes seem like "common sense", it is still reassuring and comforting to know that these concepts and principles are what the likes of Prof. Andrew Ng go by when they embark on an ML project.
To all who are working on making this platform what it is, I'm very confident that it is not an easy thing at all, so thank you so much.
por Daniel C•
Feb 01, 2018
This course provides valuable practical advice on overcoming common obstacles in machine learning and deep learning projects. Some people might dismiss these advice as "common sense", and they would be wrong! Common sense isn't so common most of the time. In other words, there are many advice and suggestions this course offers that I hadn't thought of, but "obvious" once I learned them. Well, I need to hear them, and I'm glad I took this course. BTW , the assignments are essential. You can apply not only what's discussed in the lectures, but also learn new "common sense" methodology.
por Teyim, M P•
Feb 15, 2018
The course content is very theoretical but packed with very very applicable information for improving machine learning systems. The use of simulation exercises at the end of each week really goes a long way to compensate for the theoretical nature of the course content by giving learners the ability to think in terms of a real world project and seek ways to make it better. Technically speaking, I found this course more important than most practical courses that are filled with coding exercises without any additional information around making the code perform better. Great content!!!
por Ricardo S•
Dec 17, 2017
This is a short high value course. It is especially good for someone who is trying to get into machine learning at a professional level, to avoid the usual pits of project structuring and time management. Highly recommended. It might seem less motivating, because it is perhaps less technical than other courses in the deep learning series, and does not have programming assignments, but in my view it might actually be at least as important as the more technical courses (if not more) in terms of allowing students to deliver machine learning projects in a professional context.
por Srikrishna R•
Aug 13, 2018
This course provides insights that you normally wouldn't get reading a book alone. While it does cover the core theories behind structuring of projects, what sets it apart is the truly practical tips and tricks that you could put to use in your project right away. The guidance is actionable and draws from practical experience of stalwarts rather than draw from theory alone. The test & exercise was quite innovative too as it puts you through a real world simulation to help you understand decision pathways you would take based on situational role play. Overall 5 stars!
por David T•
Dec 30, 2017
Having talked to someone who is actively working on Neural Network models, some of the insights I learned from the course looked to be helpful to them as well when we talked. I really appreciate the hands-on quizzes as well, as they gave me a chance to critically think through what I had just learned, and apply it to a real-world example. They especially helped when I got things wrong, because then I was able to rethink some assumptions I had made, and solidified my understanding of the material. I hope the next two courses are just as good as the last three!
por Donald R•
Sep 23, 2017
This course ia about the practical application of Deep Learning techniques. Andrew Ng's other courses are very theoretical and prepare you with a very strong mathematical foundation for Machine Learning. This course provides practical advice and recommendations for teams building real-world applications of Deep Learning -- advice garnered over many years of work by Professor Ng and others, and, as far as I know, not collected into a single source anywhere else.
I have taken several of Professor Ng's courses. They are all excellent. This may be the best so far.
por Vishal R K•
Feb 24, 2019
So far, this has been the most useful course out of this specialization! Sure, the others might offer more technical expertise, but this trains you things that cannot be taught in a class or a lecture. The application oriented case studies are extremely intriguing and challenging to a person whose knowledge might be completely theoretical. This course trains you to think in real life situations of applying a deep learning model, where to cut costs and effort, where to add more, how to optimize your model to surpass even the human level, and go further etc..
por kunal s•
Aug 15, 2017
It is one of the awesome courses everyone should join as by investing time for this course you may save your time in future when you are working on real world problems as Andrew has taught his experience where people makes mistakes and how to not repeat it and save your months of time,also he have taught in details about the datasets creation and there use.And also how u can use pre-trained model for other type of dataset. Join and it will make you more curious to dig dipper and also at same time making you better than some of real experts in the industry.
por Benjamin G•
Aug 19, 2018
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!
por Emily Y•
Oct 07, 2018
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
por Jairo J P H•
Feb 01, 2020
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!
por 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.
por 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!
por 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! 👍