22 de nov de 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.
1 de jul de 2020
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!).
por Wonjin K•
1 de out de 2017
This course gives great intuitions to develop deep learning model and how to go with deep learning project. I was really impressed and felt like I gain a real experiences without working at industries.
por Erick D•
22 de ago de 2020
Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.
por Maha A•
30 de nov de 2021
It is good course that gives the practical insights about implementing the machine learning problem. However, it is better to have some coding exercises to be able to grasp the idea more efficiently.
20 de ago de 2021
Very helpful tips for navigating possible problems that would likely occur while building/training a model. The "pilot-training" exercieses, that mimick real-life problems / projects, are excellent !
por Frédéric G•
29 de set de 2020
Excellent. Just one remark: sometimes I do not understand quite well the english sens of the sentence. But in overall the course is well structured and I've learned quite a few things in ML Strategy.
16 de jun de 2020
Useful to know what are the steps that should be taken after obtaining results. Tho there isn't much information regarding making machine learning projects here (ie. there isn't any hands on project)
por Antony W•
17 de jul de 2019
I am glad I took this class. There are a lot of things think about with respect to structuring your M/L project. Fortunately, it is not as mysterious as people often claim...but it is very nuanced.
por Yong H P•
25 de jul de 2018
Very important and valuable intuitions about DNN training/optimization. It's full of really practical information while implementing my own models.
DNN을 실제 적용할때 반드시 이해하고 적용해야 할 실질적 내용들로 구성된 멋진 코스 입니다!
7 de abr de 2018
It is a special lesson that guide me to think how to build a good model for ML. There is no doubt that Andrew ng taught his project experience without exception and hope that we can benefit from it.
por Amit M•
8 de mar de 2021
Excellent course to understand the various nuances of structuring these kinds of "projects". Though if the content were presented as parts of programming assignments could have been easier to grasp.
por Muhammad A•
14 de set de 2020
Andrew Ng, awesome teaching technique and well-designed course content make it easier for deep learning beginner to learn how to structure your machine project smoothly and do not lost in a process.
por Bijaya B•
7 de jul de 2020
The course was very insightful on how to tweak and evaluate and measure the performance of your model. I loved the course very very much. Hope to see more courses from deeplearning.ai and Andrew.
por Sai S B•
7 de mai de 2020
This course gave some very useful tips on how to start with a Machine learning project when I was struggling to do so. It also gave useful information about error analysis and data set distribution.
por Х. А Р•
3 de abr de 2020
I think this is the most useful course in the Specialization. Andrew reveals secrets about details which can speed up working in Deep Learning. It will help to avoid marking time in future projects!
25 de fev de 2020
Generally, the course is great. This is a short course and could be combined with other courses in this series. Also, some knowledge such as data splitting has been introduced in the courses before.
por Amey N•
12 de nov de 2019
The course gives a sound intuition and insight into the parameters to be considered and the crucial thought process involved in making the decisions for improving the performance of neural networks.
por Anshu S P•
21 de jun de 2019
Really a good course with mostly the theoretical knowledge on some aspects to reuse your model as well as some error analysis. Thoroughly taught with lots of real-life examples, thanks to Andrew Ng.
por Young L•
29 de nov de 2017
It's a great course! This course gave me a lot of new perspectives in constructing a machine learning project. Especially, the discussion of data distribution in the train/dev/test set is fantastic.
por Balachandra J•
25 de abr de 2021
Insights distilled from long experience, explained with simple examples. Probably like explaining quantum mechanics to someone who didn't arrive at the conclusion through rigorous first principles!
por Federico R•
6 de jun de 2020
I really liked this course, such as I liked the rest of the courses in this Specialization. I honestly appreciate that this knowledge is shared, accessible, and made extremelly intuitive and clear.
por Naveen N C•
11 de mai de 2020
Really a good course and got an insight into how to structure a machine learning project and some useful techniques for deep learning, such as transfer learning, multi-task, and end-to-end learning
por Chitra V•
10 de jan de 2019
Interesting course especially for ML novices. A short course and could be completed quickly, however, one needs to carefully review the lectures to avoid missing key points. Well-structured course!
por Axel D G V•
29 de jul de 2021
Although this course is the shortest one from the specialization this course have really valuable insight about how to propose a systematic way to solve a problem with Machine Learning techniques.
por arvind s•
3 de mar de 2019
While there are lots of techniques out there, this course really helps you gain a different perspective on the bigger picture and teaches you to avoid some of the common pitfalls you may encounter
por Max B•
21 de nov de 2018
Fantastic! As is pointed out by Andrew Ng, the material is not taught in other courses. You will get answers for a lot of things you might have wondered when working on a machine learning project.