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: yi-chun t•
Andrew again delivered a great course! He talked about his real-world experience in creating and delivering ML projects in this course that is super helpful. I never see any other instructors who cover this important aspect of ML projects.
автор: Kursat P•
Great course! It would also be very helpful to have the final slides of handwritten notes by Andrew as .pdf files to take a look at the content later without having to watch the videos. This course is extremely helpful. Thanks to all of you!
автор: Edson C•
this course is very wonderful. I was really impressed with the amount of information acquired and that will be very useful when developing my ML applications. thank you very much for these many years of experience transmitted in this course.
автор: Tanmay K•
Amazing course taught by Professor Andrew Ng. I love the piratical insights that I gained through this course. I believe this course is a must have for someone who wishes to tackle deep learning problems in a systematic and organized manner.
автор: Cyprien H•
Very instructive course, full of practical and actionable advice to focus on the right problems in an ML project. The "flight simulators" are concrete examples of decisions one has to make in an ML project and it is good to practice with it.
автор: Jean V•
It does a great job of providing guidance on how you would plan a deep learning project. Transfer learning in particular is a very intriguing approach to leveraging previous work to speed up training a new neural network for your new task.
автор: Jeroen M•
Material is excellent, Andrew is a brilliant teacher. Learned a lot. (Minor complaint: week 2's questions are formulated in a confusing way, making it hard to answer correctly even if you've understood the material of the course perfectly.)
автор: Deleted A•
@Andrew Ng: Your statement "And I think that phonemes are an artifact created by human linguists. I actually think that phonemes are a fantasy of linguists." in: Whether to use end-to-end deep learning" Week 2, ROCKS !!!! GREAT and agree...
автор: Dr. M E J I•
This is an excellent course for anyone in Deep Learning, Data Science, or Machine Learning. It is a little on the short side, but packed with good ideas about how to structure your projects when considering various differing data scenarios.
автор: Prerana H B•
Exceptionally good course.It gives brief idea of how and what strategies should be used while approaching any problem or building the system .Also gives idea about how to improve the efficiency of already build system and upto what extend.
автор: rahul m•
Excellent course that discusses a lot of details and nuances about machine learning and deep learning that are drawn from Andrew's own experience as a prominent researcher and pioneer in the field . I feel I gained a lot from this course .
автор: Madagama G B S•
This course helped me to systematically analyze errors in deep learning implementations. The machine learning flight simulator is a great way quickly learn how to address issues you would face in making practical machine learning problems.
автор: Fawad H•
This Course is best for all level and it teaches in the best way to how to make your project to do well and how to suggest solution and how to detect problems in the training of the neural network. Thank you Andrew for making this course.
автор: Yingxiang Z•
Very useful introduction to the real applied machine learning procedures. This course enables us to know exactly what steps to take in different phases of a project, and could potentially saves us a lot of time by avoiding useless efforts.
автор: Wong C H•
"Experience can only be learnt by practicing" This course showed us some useful scenario which I think is very likely to be encountered in future projects. I think this will help to save time to develop deep learning model in the future.
автор: yugandhar n•
Initially I thought It would be boring. But after taking the course, I feel the difference. Once again, Andrew Ng rocked it with composition of this course and quiz. I feel this is must course in deep learning, who is working in industry.
It's a theoretical approach of Machine Learning projects that gives a lot of awesome insights of many real world problems that you face when building your model. It's a short course with great insights ! I definitely recommend taking it.
автор: Khaled J•
Excellent class with practical advise to accelerate the application of best practices based on Andrew's experience. I would highly recommend this to practitioners wanting to save a lot of time learning these best practices the hard way.
Excellent course on understanding how and what to prioritise in ML projects. Not just helpful for people leading ML teams, but also for people who are doing some independent projects. ML is a lot of fun when you do experiments for fun :)
автор: Mohamed C S•
Excellent Course, though it is an optional course, it is really worth taking it!
The Use case studies are just excellent! You can really have a taste of the problems encountered when you have to manage a deep learning project. Great work!
автор: Omid M•
Minor issue: often the request for feedback for a lecture came right at the beginning of the lecture, covering big portion of the video ('was this video helpful'! ). It was annoying (I couldn't figure out how to minimize it).
автор: Ashish P•
Amazing course. This course is really a practical understanding of what DL is. Apart from learning the algorithms practical aspects are very necessary for DL and this course provided me with the same.
Thanks to ANDREW NG for this course.
автор: Long C•
Great and detailed strategies especially for people working on a machine learning projects. With good strategies, time and money may be saved. A really good complimentary material to Andrew's new digital book: Machine Learning Yearning.
автор: Aditya G•
Highly recommended as it helps one think how to improve their ML models. Just do a 60/40 split and hoping for the best result is not the way to go, and this course definitely helps unveiling how to remove bias and variance from a model.
автор: Chulhoon J•
this course has very practical and helpful advices to solve problems related to the deep learning algorithms. I believe those valuable advices and tips will be able to reduce tremendous times and efforts when you stuck with the problem.