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 analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
por Mark M•
This course is at all an important part during the learning journey. The only reason why I not rate full 5 stars that the recommendation ramen little bit on high level and do not address typical frame conditions in real world projects.
por Oliver M•
Lots of practical stuff about training models. But you should try building a few models before doing the course. Otherwise, you may not fully appreciate how much time can be wasted unless you use Andrew's clear and logical approaches.
por Wei Z•
Lots of interesting and useful idea. Unfortunately the editing is poor and Professor Andrew Ng has gone a little bit repetitive in his talking in this course only. The two previous courses were great but this one is kind of dragging.
por Saad T•
I am a big fan of the jupyter notebook assignments. I can understand that it could be hard to build python assignments for this course, but not impossible I think (maybe around error analysis, impact of artificial data synthesis...)
por S A•
The content of the course lecture is great. The teaching is great. One problem is the quality of subtitles. The black background does not allow to see what is shown behind. It would be better if the background would be transparent.
por Sarah W•
Great material! Some of the videos went a bit long, and I think the point could have been made in much less time. However, overall this series has been great and I still got some very valuable info out of this course, so I'm happy.
por Michael A•
The course was very well structured and Andrews explanations was wonderful as usual. The only thing I was missing was more practical hands-on in the form of a programming exercise or two to really demonstrates the different ideas.
por Hanling S•
Andrew really provided great content, but the edition of this course is not as good as the first two, sometimes you will hear some repetitive sentences or a long pause. Hope they can upgrade this part, all the others are terrific.
por Cheng J•
This course give a lot of useful practical advices on training a machine learning/deep learning models. However, some of the advices are rather subjective and experience based, and some of the homework answers are quite debatable.
por ashwin m•
this course provided very interesting insight into missing , incorrectly classified labels and also how existing models can influence the training of a new model which is on similar lines as the task the existing models performed
por Jithin V•
Great course for machine learning strategies in deep learning.
Several concepts which aren't discussed in other courses have mentioned .
Especially the new way of splitting the datasets, transfer learning, multitask learning etc.
por Silvério M P•
Looking at practical examples is an enormous help and some concepts i learned here will undoubtedly be useful in the future, i just think there should be more of it. It's just really short both in duration as well as content
por Vignesh S•
It was really good to know how to structure and tune the nn so as to achieve a better model. But, I felt that it had too much theory in it that is hard to remember every time a model is to be designed. Overall, it was good.
por Rahul P•
One of the quick and great course for individual and team for understanding how to handle and structure the machine learning project. how to improve accuracy and handle error such a wonderful course made by deeplearning.ai
por chandrashekar r•
I rate the course high. Unfortunately many of questions (posed in the forum) have not been answered.
Her are some suggestions:
Have quiz after every lecture. That will firm up the concepts.
Give lesser help in assignments.
por Gustavo S d S•
Gives a sense about improving the performance of Deep Neural Networks, with error/bias/variance/data mismatch analysis. However, there is a lack of hands-on exercises, not having a programming assignment, only quizzes.
por Michael F•
Lots of useful tips and tricks in this course. I feel that the videos could have been a bit shorter, and it would have been nice to have some programming assignments. Overall the course was extremely useful, however.
por Grant G•
A pleasant diversion into practical considerations of project design. However the lack of programming assignments and the somewhat vague and fiddly quizzes make this a less satisfying course than it could have been.
por Jeffrey D•
This was a good overview of the concepts I have already learned. It was a good refresher on progress and changes in training best practices. There are a few flawed questions in both quizzes that need to be fixed.
from my perspective, maybe, it would be better if this course is the end course of the specialization. the contents are greate. I would like to suggest others to put this course in the end of the specialization.
por Othman B•
Very interesting, but too short. The aim of the course is to provide a good overview of the different situations occuring in a project, but there is more questions arising. Experience will come with training.
por Antti R•
nice to follow, but I would have liked it there would have been more variance. e.g. quizzes breaking the videos. I'm basically comparing this experiment with the other courses made by Andrew/deeplearning.ai
por Samuel C•
A useful few hours of videos. I found the questions quite useful, but overall feel this project would have been better off being spread across other weeks, as it doesnt work so well as a stand-alone course.
por Sardhendu M•
Very practical. programing assignment using the concepts would help to solidify the concepts. I would really appreciate programming assignments on Transfer learning since a lot of industries practices it.
por Wiebe V•
Clear course, it would have been helpful to add notebooks to the course to have a more realistic feeling of the problems. This would make it also more clear how the dev set influences the training phase.