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 Amir N•
30 de mai de 2021
This could be a more useful course if it came after convolutional neural networks and sequence models courses. In that case, the learner could practice some of the strategies on the models that he had previously developed. Right now, most of the strategies will be forgotten by the time that the learner reaches to a point that can confidently develop large deep learning models.
por Mikko H•
24 de set de 2017
Great material that's clearly based on valuable practical experience. I and found the "machine learning flight simulator" quizzes to be an educational format. However, the editing of the quiz questions (grammar, matching question types with wording in the question etc) was not flawless in September 2017. This course would benefit from another review pass from this perspective.
por Kévin S•
31 de jul de 2018
This course is clear, and show how a machien learning project should be driven. But there is two problem : First it is entierly theorical : no pratical exercices (so it is only 4 stars) ; second it did not speak of a big problem : How make your boss understand that if you use the "test" set too mush, it become another "developpement" set -> without using sciences words...
7 de mar de 2018
Overall, a great course for designing deep learning projects, which gives a lot of insights and tips that typically not taught at university classes. However, there does exist some minor problems related to video editing and quiz problems. I suggest the lecturer or staff of the course put more efforts in dealing with them (and maybe attend more to the discussion forums).
por Yen-Chung T•
25 de set de 2017
This course gives an overview on how to address common problems faced during machine learning projects. Although these experiences can prove valuable, for average people that may not be actively involved in machine learning, the information may sound like "common sense". The course may benefit with a more abundant set of real-world practice scenarios for analysis.
por Saurabh D•
2 de abr de 2020
This course was totally different from the previous two courses. It was focused more on the theoretical aspects of how to approach and build ML projects, difficulties that ML engineers can face and how to avoid them. The content of this course could have been more interesting if more real world problems were included and if there were some programming exercises.
por KUMAR M•
11 de fev de 2020
A very nice course to teach how to start a data science project, how to evaluate it, how to select path ahead improving the model, what all to be taken in consideration before training or while training or after training.
Some more case studies could be added since the course is smaller in length and case studies are helping a lot in making understanding clear.
por Carlson O•
20 de out de 2017
Again, great course. Congratulations. This time, i've missed some programming assignments, although the case studies was very instructive of the practice, some programming experiments with transfer learning will be great. Nevertheless, the course has extremely valuable knowledge to those, like myself, that want to practice in real problems and corporate world.
por Carlos d l H P•
27 de jan de 2020
Actually adds some insights I hadn't learned (or at least I was guessing but it's always nice to have a double check) after 4 years as a data scientist.
Also, some of those insights are very specific to neural networks projects, so doesn't matter how many years have you been working if you've never made deep learning projects this will help you nevertheless.
por Ching-Chih L•
17 de mai de 2018
This two-week course gave very important concepts. However, there's no programming assignments and lectures are lengthy. It felt a little "boring" for a hand-on guy like me.
That being said, one should not skip these important lessons if he/she wants to take charge of ML projects one day instead being a programmer who only takes orders from others for life.
por Arthur O•
28 de fev de 2018
This course gave a lot of practical advice and is excellent material to combine with the more programming-focussed lectures of the deeplearning.ai series.
Small points of criticism are that I thought some videos could have been a bit shorter/less repetitive and there were quite a few language mistakes in the quizzes (missing words and grammatical errors)
por José D•
26 de set de 2019
Course 3 of the Deep Learning Specialization. There is no coding in this one but longer quizzes which require you to fully understand the concepts and recommendations given in the course. It's all about ML project strategy and how to manage you results and errors. Quite interesting and important for the general understanding of a Deep Learning project.
por Chris L•
6 de set de 2017
I liked this course overall and found it to be very informative. I, personally, was a little thrown by the eclectic nature of the course's materials. Sometimes it seemed as if the material covered in each week was only loosely related, or was thematically similar for part of the week, but then the last few videos were on something else entirely.
por P S R•
15 de nov de 2017
It is too much of theory, with significant repetition from machine learning course and within Deep Learning course 1 and course 2. It would have been lot of help if we had programming exercise on transfer learning, data synthesis and multi task learning to get a hang on practical experience, similar to first 2 courses of Deep Learning!
por moonseok s•
10 de jun de 2018
thank you for to teach how to research and it will be of great help to real researchers.All theories have been a pity not try because I did not get a lot of the actual study. I think it will be a great help for future research opportunities.It is very difficult to study because it is not practical. but in future it will very helpful.
por Jesús A G Z•
28 de jul de 2020
Although purely conceptual, the course really gives good advice on how to come up with a work flow to react to errors due to data, and the metrics that can be used as reference. I just wish there was an assignment where you could see a NN working with mismatched data and how it reacts to some of the improvements that were mentioned.
por Anshul M•
31 de out de 2017
Course contents are great as it talks about how to improve performance by giving real world example. This is one of the most crucial pieces in any model building task, but still is less focused in traditional courses. Andrew Ng's team has dedicated a full course on this aspect, which I believe will do the learners a huge benefit!
por John R•
5 de ago de 2019
The quizzes were a little annoying to get through, as it is not much about deduction or reasoning, instead it's about learning the advice or rules mentioned in the videos. I think an actual implementation of a learning project and applying the error analysis, transfer learning, etc, would be more beneficial for the student.
por debraj t•
10 de mai de 2018
Gave me a broader and more strategic perspective on how to structure and run a Machine Learning project.
I just felt this course came too early in the learning process. It would have far more relevant and useful had it been a more downstream course.
This does not take away from the fact that the content is very relevant
por Kang C•
23 de out de 2022
Material is great. Quizes could be better imo. Sometimes I get the questions wrong not because I don't understand the materials but because of the choice of words in a question. Also would be better if there are actual real world case studies instead of just going over the concepts of how to structure a ML project.
por Uğur A K•
15 de nov de 2019
This was a good course because it "kind of" prepares us to real world projects and we think about what to do when different problems arise. I would also really like if this course included a section on how to create datasets from images, sounds etc. and prepares us for the "boring" parts of machine learning as well.
por Jacob B•
3 de mar de 2022
This course provided some interesting strategies and advice on how to start and struture machine learning projects. My only complaint is that this course should be placed at the end of the specialization. I felt like I wanted to know more about deep learning models before I learned how to strategize on deployment.
por Zahin A•
29 de jun de 2020
Was extremely helping in providing ideas on how to start and work on machine learning projects. Provided clear and well thought out ideas on how to make the most use of time and data. A small improvement can be made to the course by dividing some of the contents of the course to another week for better structuring.
por ANIL V•
17 de jun de 2020
Course is great. All concepts are explained very meticulously. Lots of respect for Andrew NG. Just a small suggest please don't give more examples on cat classification. Autonomous driving case study was good, speech recognition examples are good. Please give more realistic examples, that can be used in interviews.
por Ranjan D•
17 de jul de 2019
Great explanation on how to structure your machine learning projects like distributing data among train & dev/test set then what to do for each type of errors to continues to transfer learning, Multi task learning, End-to-End Deep learning. It has been a fantastic journey learning about these different techniques.