Jul 02, 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!).
Mar 31, 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.
By Gabriel L•
Aug 25, 2017
I've done a Master degree in IA and the things covered in this course have never been addressed by any of my professors. Now I've been working in a Machine Learning team for the past two years now, and I believed these lessons would have been of great value, and would have saved me a lot of time!
By Ruben G•
Aug 07, 2020
This a great course on Deep Learning, the contents are so full of interesting information, actually, this course could also be called "Everything you wanted to know about Deep Learning (but were afraid to ask)".
As always, Andrew delivers a great course, whose content is ready to put in practice.
By Julio E H E•
Jun 16, 2019
This course is very helpful to learn best-practices and problem-solving strategies that can help improve our deep learning algorithms. While I think the ultimate way of learning is through practice, here you can at least get a list of things to try in the future as you work on these algorithms.
By ANSHUMAN S•
May 26, 2019
Although this was a bit hard for me to understand but still through the quizzes i got an insight so as to where will these advice be applicable and where i can use what i studied.
I am thankful to the teachers and a especial thanks to Coursera for giving me the opportunity to avail this course.
By Virgilio E•
Dec 17, 2017
I think this part of the specialization is a great value key, and makes the difference with other courses, self learning books, etc. The contents of this individual course helps a lot into understand and improve knowledge studied in previous and next courses. I definitely recommend this course.
May 06, 2020
This is the one that talks a lot about how to struture DL projects. And many methods have been taught in this course including focusing on the error control, transfer learning, multi-task learning. After learning these methods, tuning a DL project or starting a DL project will be a lot easier.
By Andrei N•
Sep 21, 2019
The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.
By Neil O•
Dec 08, 2017
This is a unique course that provides invaluable perspective on how to direct a deep learning project. Its value is derived from understanding the performance metrics ( the data about the data) and acting in a data driven way. Anyone in charge of a deep learning project should take this class.
By Diwakar P•
Jul 10, 2020
This is really a greate course taking ont into the deep thoughts of how to structure deep learning projects. I teaches use how to analysis the various errors like human/bayes level error, training error, traing-dev error, dev error & test error, I learnt to anaylise errors and take decisions.
By Md R H M•
Jul 16, 2020
The lectures are arranged in a concise manner with only the necessary details. This is a shorter course than the other courses, but I learned a lot about different strategies of ML. The case study is also of top-notch which helped me to introduce to the application and various aspects of DL.
By Mahmoud S E•
Oct 23, 2019
It has the best practice tips and top secret advises for Machine learning.
It really simple and clear. I love it too much.
Especially, the exams, A lot of effort is done on it. And the instructors notice which best way to absorb this deep concepts in this course by flight simulation techniques
By Virendra K Y•
Apr 05, 2020
Thank you so much team and NG sir. What a simple explanation of everything. Love you guys and god bless you and your team sir. Honestly, no word to say how simply NG sir explains all the concepts. Hard work team. Love from India. and do yoga to boost your immune and stay safe from Covid19.
By Charles B•
Jul 21, 2018
Covers some interesting points, particularly around introducing external data to your training set that doesn't match the distribution of the dev/test sets. Andrew Ng offers practical advice for running projects using Deep Learning techniques and how they differ from traditional approaches.
By Tanay G•
Feb 04, 2020
I was sceptical at first, it seemed that the course would just teach a lot of theory which won't be relevant. I am happy to say that I was wrong, the course gave me a better understanding of how to take various decisions for a particular machine learning problem. I liked this course a lot.
By Akash B•
May 14, 2019
It teaches the decision making process whenever you're working on a real- world probelm. You should grasp all the ideas into your brain very well. I think this is very important as per in the field of deeplearning.
This course is very rare, and it provides best case scenarios to test with.
By Haoxuan Q•
Jan 26, 2018
I love this course very much and I would strongly recommend this course to other DL colleague. It is truly that DL is a highly empirical process which needed to be more systematic. In this course, I have learned many methods to make DL more controllable and predictable. Nice Job! Thanks!
By K R•
Jul 09, 2020
Well, once again I'm so happy and very satisfied of the content proposed in this Course by Prof. Andrew Ng. Thanks a lot for this valuable content and for always making it easy to understand. I'm getting more and more knowledge in DL which happens to be very usefull for my Phd project.
By Pedro f•
Mar 23, 2019
In my experience with Machine learning, we usually spend more time checking the algorithm than checking the best distribution of our data. In this course, Professor Andrew teaches us the need and obligation to create a correct distribution of our data with examples from the real world.
By Mohd S A•
Feb 28, 2018
Extremely helpful for a beginner so as to think like a machine learning problem solver. I think there should be more quiz added to this course with scenario like given in two quiz. I have never enjoyed any course so much by taking same quiz again and again to get better understanding.
By Hiep P•
Dec 14, 2017
In the bloom of Deep Learning/Machine Learning industries, know how to build a project is more important and a priority to know what knowledges to build that project. Break the problems, take each step follow the guide and avoid common pitfalls in process, this course will satisfy you.
By Javier H E T•
May 01, 2020
this is definitely the best course i had taken. it has just 2 weeks, but it was the hardest. i will definitely come back to see the teachings here explained to check up if i'm thinking correctly so i don't make much mistakes in taking a direction in projects.
By Elena P•
Sep 01, 2017
The case study format for quizzes was highly effective in helping me uncovering gaps in my knowledge that I didn't know were there. I would have liked to see at least one more case study per week. One per week just wasn't enough.
Overall good course with a few minor video glitches.
By Carlos A B R•
Jul 22, 2019
I found this course really interesting because it gives many details on what path to follow to achieve better results not only depending on the amount of data we have but also taking into account some small details that can make a difference when starting machine learning projects.
By Dharam G•
Jul 02, 2018
A very well systematic approach explained, to structure ML projects.Can be grasped and implemented by anyone, let it be a beginner or some expert.Really liked the idea of case study in quiz. (Wait ! How about extending this idea into some coding exercise ? Would be some real fun !)
By Andrew M•
Oct 11, 2017
There is no coding in this course, but you learn a lot of how to design a Deep Learning Study. I learned a lot about the distribution of Training/Dev/Test sets and how to diagnose problems when a neural network is not performing as well as anticipated or if it is performing well.