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!).
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.
автор: Marcio R•
Excellent course overall! The course structure is very well made, Andrew is an amazing teacher and explains everything in a very detailed and intuitive way. The tests are a great way for practicing what was explained in the lectures. Strongly recommend this course to anyone interested in the topic and that have the required background.
автор: Subhasis M•
This is an excellent overview of the points that someone taking up an ML/DL project should keep in mind. Though this is not a comprehensive guide, which is understandable given the stipulated duration online courses like this are meant for, this is a definitive guide to give someone a nice head start into structuring his ML/DL project.
автор: Pablo G G•
Nice intuitions of what to do when you need to improve model, and believe me, you will! :D If you set up your local jupyter lab and start playing with deep learning, you will quickly see that this course is gold in order to optimize you DL algorithms!(its all about getting that loss to 0.0000001! :P) Don't understimate this teachings!
автор: Dmitry R•
This, in my opinion, is the most important course in the specialization! It teaches you how to plan your machine learning project, which errors and challenges can rise during implementation and how can you deal with them. Personally, I feel it helped me a lot as I currently try to plan my machine learning project as part of my thesis.
автор: Fasih U•
I learned a lot about different strategies to chose for getting fast and much better out come from this course. Also downloaded the book mlyearning written by Dr. Andrew. So that i will have all this in my hand when i will need this strategies to review. Thank you Andre Ng for giving this much information. You are the best I love you.
автор: Ankit K•
thanks for providing good insights on how to approach a machine learning application and where not to waste valuable efforts. I think Mr Ng has been very thoughtful to setup the structuring part as a dedicated course which highlights the importance of setting right goals and not to lose our direction during the development iterations.
автор: Kunjin C•
Compared with the previous two courses in this special, this course is more practical and useful when we are actually trying to solve real-world problems. After taking this course, one will have a clearer mind in terms of making the most out of data from different sources as well as coming up with better solutions to certain problems.
автор: Cristina N•
Absolutely LOVED this course: with the two "case study" you can really get a sense of what does it mean to set up a real ML/DL project and how to address the problems you may (and you're very likely to) face by building up or leading a ML/DL project.
If you're thinking about learning Deep Learning, this course is absolutely NECESSARY!
автор: Tesfagabir M•
This is my third course in the deep learning specialization. I have learned a lot related to different strategies with machine learning projects. The concepts are easily explained with practical examples. The assignments are also very helpful for applying in real machine learning projects. Thank you professor Ng. You are the best!!!!
автор: pedro o•
This is a great course for anyone new to machine learning. It focuses on the core challenges one may face while carrying out machine learning projects. Overall, it is a must take for people new to the field,professionals,hobbyists,etc .Thank you Andrew Ng for being a great instructor, I look forward to completing the specialization.
автор: Ayomi A•
Excellent course and very interesting !!
Allows you to analyze real ML problems and supports you with the basic and essential skills needed to develop ML algorithm and evaluate its performance and how to approach the issues that one can encounter during the iterative process, what are the options, which is the best to go with, etc.
автор: Ayush P•
Really good course to develop an approach to NN problems. I thank you Sir Andrew Ng for all the courses that you have made available on Coursera. It has been an really awesome experience learning about neural networks from you. I will finish the remaining courses and recommend it to people who want to pursue a career in ML and AI.
автор: Jingxiao Z•
This is a practical course, extremely helpful for those who have met so many troubles in realworld projects. It is quite helpful for startups, where we can implement those ideas immediately. On the other hand, the transfer learning and end-to-end learning paradigms might be very useful but challeging in big companies and sectors.
автор: Nouroz R A•
This is one amazing course because it exposes you to a 'real' ML/DL problem. As a newbie I learned a lot and hope that in future I will once again do it as a ML research/development Engineering Manager. This is something very practical and now while doing big projects I will consider the learning of this course. Thanks Andrew Ng.
автор: Mohab S A•
Exceptional, one of a kind strategic course for ML practitioners. The amount of wisdom and knowledge shared in this concise course would definitely save any budding ML engineers from the common pitfalls that many teams may still face. It also sets the foundation stone for cultivating prospective machine learning project leaders.
автор: Mirna M A•
the best course course so far in terms of (error analysis, how to deal with training/ dev/ test sets and what the symmetry of distribution means, how to split data set in the best way, how to be able to use an algorithm again in another deep learning project, how it's important to correct the incorrectly labeled data set, etc )
автор: Hermes R S A•
Consider this a course on best practices. I found fundamental advises on how to best carry a ML project from scratch, regarding the first model you should choose, how to perform on different scenarios, how to choose systematically your train/dev/test set and so on. The project simulator is a must, I wish they put more of those.
автор: Shazib S•
Really really good course. I never knew about the intricacies of error analysis that is done in ML/DL projects. This was a very insightful course. Would see the lectures again if I need to (which I will). Nevertheless, amazing course. The content is explained in a step by step and appropriate fashion for even a newbie like me.
The teaching in this course is so invaluable for interpreting the results. Now, I believe I can understand my models' accuracy based on professors teaching. The professor teaching contains unique knowledge and experience, where you can't reach via the internet, library or asking your university professors. Thank you, Prof. Ng.
автор: Shehryar M K K•
I think this course was very valuable in teaching insights about how to think about and formulate ML/DL problems. The case study quizzes were really good and made you think. I hope coursera expands on these case study quizzes for future version of this course as well as introduce them into other courses of this specialization.
автор: Alessio G•
This course is a summary of Andrew's experience. I've yet listened this nuts and bolts from Andrew speech(you can find it on youtube) but there are some precious advice that are so much valuable. I'll recommend this course to everyone who want to start a carer in DL. Big thanks to Andrew, the Deeplearning.ai team and Coursera.
автор: Dejan Đ•
Plenty of wisdom shared by Dr. Ng here, presented in a very digestible and actionable fashion; can't wait to apply to approaches suggested to my own projects. These kinds of courses are golden, can't find such practical knowledge in ordinary textbooks. Thank you for the course, can't wait to continue with the specialization!
автор: MBOUOPDA M F•
This course taught me recipes about conducting a machine learning project. I'm now more confident about being a machine learning project lead. The assignments are interesting because they are case studies of real situations, where decisions need to be taken in order to iterate and converge to a better machine learning model.
автор: ankit d•
This course really help me to understand exactly how to make decision to distribute the data sets, what to do with the new data set, how to examine the error, how to use previous model as a transfer model for other classification, what is multi-tasking and many more
Thank you for your support and sharing of your knowledge
автор: Arvind N•
This course was most useful as Andrew explains practical engineering challenges and valuable tips to overcome them!
As a technology architect, I am more interested in predictable, guaranteed results and can guide my my ML engineering team to make the right choices in given real-world uncertainties and engineering challenges.