May 31, 2019
I have learnt a lot of tricks with numpy and I believe I have a better understanding of what a NN does. Now it does not look like a black box anymore. I look forward to see what's in the next courses!
Sep 02, 2019
I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)
创建者 Mihai C•
Jul 15, 2019
Very well structured, the code is much better than in the Machine Learning course that was initially posted on Coursera, and the use of Python instead of Matlab makes things much easier and friendly for everyone. I really enjoyed it.
创建者 SAGAR B•
Sep 10, 2017
A great course to understand basic concepts of Deep Learning. If you are a beginner in Deep Learning and thinking if you should invest your time and money here, don't give a second thought and join right away. Andrew Ng never disappoints!
创建者 Benito C•
Sep 02, 2017
Very hard work in designing the notebooks so the pupils's learning processing is maximized.
创建者 William M•
Sep 04, 2017
I really enjoyed taking this course. I have taken one of Andrew's courses before, and they keep getting better. I have a background in development, and appreciated the use of python over octave. Andrew consistently strives to provide an intuitive feel for the topics he is presenting. The fact that he is able to provide a complex subject in a simple manner speaks to his mastery of the subject.
The course contained a great mix of theory and practical application of those theories. I'm looking forward to the next course.
创建者 Kieran S•
Oct 22, 2017
Extremely well structured course that gives you good intuition about how deep learning works by starting with simply examples and adding layers of complexity.
Aug 23, 2017
创建者 Abdessalem H•
Dec 03, 2017
This is one of the courses I enjoyed the most. For someone who has little to no knowledge in calculus and programming, I found the course is well tailored for all kinds of background. The pace is not so fast and Andrew is making it so easy even for beginners to grasp the new jargon and formulae. Thank you Coursera. Thank you Andrew.
创建者 Anjan D•
Oct 01, 2017
Excellent course with great assignments. I have learnt from the beginner level in DL. It also helps one to brush up the calculus and linear algebra knowledge very much.
Aug 25, 2019
This course was really clear my concepts of Deep Learning and how actually neural network works.
创建者 David R•
Oct 01, 2019
Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.
There are a few shortbacks:
1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.
2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).
3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.
4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.
5 - Slide graphics and Andrew handwriting could be improved.
6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.
Again overall - highly recommended
创建者 Stephen K•
Nov 07, 2019
Tying your shoelaces is easy...if you have two hands. Some reviewers say this course is easy too. But you will be confronted with multiplying matrices and some differentiation. More than anything, I found it difficult to keep track of the different matrices, and particularly their dimensions, which if you do this course you will see is vital. There's also a lot of notation to overcome. You will need to understand some python, particularly how to extract values from tuples or dictionaries, and being familiar with user-defined functions will also help. So, easy?
The course starts with a 0-level neural network and builds up to a deep neural network. It's a nice way to easy yourself into what is clearly a complicated subject. The downside (at least for me) was that each week I was hit by yet more new notation, and I felt that some of what I'd been taught in the previous week (and was clinging on to by my fingertips) was almost redundant. It made my head spin. Nonetheless, I persevered and passed the course.
So, I've gained an appreciation of approximately how a neural network works. I could not build a neural network from scratch without massive recourse to my notes and assignments, and plenty of time. Is this how people build neural networks, or are they using libraries to make the job much easier (Tensorflow, Keras, etc.?) Or, can I use the final assignment as a template and apply this to many problems? I don't know.
I thought the notes were quite poor. There is a mountain of writing on most slides at the end. I scribbled more notes to explain Andrew's notes, otherwise a week later it'll be clear as Aramaic. However, Andrew repeats and explains well what's happening. He has a calm and reassuring manner, which I really liked.
People have complained about assignments being too easy. Not for me. I thought they were a good way to reinforce the lectures, and provided a means to see how a neural network could be built in practice. The assignments are more like lectures with your participation than traditional assignments. This is a plus point, in my view.
Finally, I'm still blown away how just a 'simple' logistic regression with sigmoid activation function can predict cats from random images so well. I've done the course, but it's like magic!
Sep 09, 2017
The content is great and I learned a lot. Certainly there could be a lot more feedback by the instructor in the forum. My feeling is that the students are really left on their own. Good from one point of view (cause you really have no choice than crush your head on the problem for days until you understand or give up), bad from another (it takes a lot longer to clarify difficult points). Fortunately the forum is populated by very clever students that take the time to answer questions. As a beginner I learned the broad strokes and intuitions for NN in this course, but the details about certain formulas are still very obscure and I was hoping for a better explanation of those.
创建者 Omar A•
Jul 22, 2019
If you have taken this course after ML by Andrew, you will see exactly the same material covered in 1 week expanded in 4 Weeks except using Python instead of octave or Matlab.
If you have calculus background I expect you to get tedious from elementary approaches in the lectures to get rid of Math and calculus.
Programming exercises in this course are very easy and below the level of first excellent experience with ML course.
There is no easy way to get lectures slides, No reading sections in this course. Like this course made to make systematic approaches to get things done without actual care about understanding the theories and concepts.
The good news comes when you have no previous knowledge about NN and elementary python skills, then this course is an excellent way for you to start.
创建者 Francis J•
Dec 29, 2017
too easy, suitable as an entry level class
创建者 Muhammad A•
Aug 20, 2018
Great attempt but it failed to provide complete details. Specifically the project files and their loading mechanism
创建者 Ofer B•
May 01, 2018
Very abstract, and the examples are not as concrete as they could be. I'd use better visuals to ensure that the concepts in each video are understood 100% visually.
创建者 Aratz S•
Feb 27, 2018
Easy course if you have coursed the ML course before. I would like to see more explanations in detail. Still some bugs in the assignments... why???
创建者 Tobias G•
Feb 21, 2018
Few Detail. Mathematics missing.
创建者 Jérôme B•
Nov 16, 2017
To me, this is a failed attempt at simplifying those concepts. After spending hours trying to figure it out, now I find the algorithm behind the Neural Network very simple, and I can easily explain it to someone. But in this course I had to figure out by myself what was the point of those hundreds of lines of maths. So, very interesting concepts, but the "transmitting style" wasn't for me.
Apr 10, 2019
This course is really good but assignment given to solve is not understandable.
创建者 Joseph K•
May 20, 2018
It will be a good course when you dump jupyter note books.
创建者 Felix F•
Dec 20, 2017
giving low grade for ongoing delays of course 5
创建者 Long H N•
Dec 10, 2017
创建者 Maxence A•
Oct 29, 2017
The programmation exercice are nice, but the courses are mainly about very basic linear algebra.