I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!
Really, really good course. Especially the tips of avoiding possible bugs due to shapes. Also impressed by the heroes' stories. Genuinely inspired and thoughtfully educated by Professor Ng. Thank you!
por Halil D•
Learning from reliable resources is crucial. Andrew Ng is ranked #3 in the field of Deep Learning, in terms of the number of citations, on Google Scholar. Therefore, being able to learn from a person like him is an extremely valuable chance. I learned a lot, but would like to tell the things that should be improved:
• There are lots of redundant repetition. It kills the flow and creates a serious mess
• Assignments are only focused on finding a few missing lines in the cells. Therefore, it cannot evaluate whether you "understand the big picture" and "can build a model on your own" or not
• Sometimes terms/concepts are not clearly explained OR not explained at the right time. Example: A new term "activations" comes up in a video, and you wonder what is that. However, you learn what actually it is, maybe in the next video by your own inference
Advice for learners: Before starting to a programming assignment, download the whole folder of this programming assignment (you cannot download a folder, but you can download it file by file and create the same folder with its original structure) and work on your computer. By this way, you can prevent the "kernel disconnection" risk of the online version, and also replace the notes within the "Markdown" cells with your own summary. When you complete the programming assignment, you will just need to copy the codes within the "Code" cells to the online version, and then submit
por Shrihan D•
Fantastic course, great for newbies to get into machine learning; however, some prior experience with basic statistical learning algorithms (linear regression, logistic regression), experience with basic linear algebra (vectors, matrices, matrix multiplication), and experience with multivariable calculus (chain rule, partial derivatives) is required to extract as much as possible from this course. For the programming exercises, it is required to know the fundamentals of python programming (OOP is not necessary and the course teaches you NumPy as you go along). The programming exercise in the final week went a little bit over my head with the caching of forward propagation values, but it was nevertheless a great course. On to course 2!
por Akif E S•
I think while writing helper functions, expected outputs' should be same as our test and train data. It causes some misunderstandings. I know the fact that when we don't use assess' it will take time to see output but I think that this is a sactificial thing.
And also for the students that know calculus well, optional videos' can be much more detailed like dZ computation or the concepts of deep learning via calculus.
Except these two reviews, I think this was a really good course. I really thank you to you who prepared these courses.
My best wishes.
por Nowroz I•
I loved this course as it explains the intuition behind the methods used in deep learning. As I have no problem with Calculus and Linear Algebra, I was able to calculate the derivatives by myself. People who are not accustomed to working with NumPy may find the assignments overwhelming. Hence, my suggestion will be to learn the NumPy (only the basics will do) before starting this course.
I give four stars because the course is great and the programming assignments too. But I think sometimes the programming assignments were a little condescending and easy. Don't get mi wrong, there were moments that I din't know what to do, but there were also a lot of times that all the procedure was explained.
This course was really clear my concepts of Deep Learning and how actually neural network works.
por Shravan V•
The course exercises were very well thought out and well designed. The instructions were not crystal clear, which led me to errors in the notebook. In week 4's last assignment, it wasn't made clear that the function definitions I had written in the preceding assignment should not be cut and pasted into the notebook, but that the grading system would use its own function definitions; this led to my submission leading to grading errors. Took many hours to figure out what was wrong, through the help of one very helpful person (Paul Mielke) on the forum.
Andrew Ng's handwriting is TERRIBLE. He should either practice writing more clearly, or use slides.
I would have appreciated having written down lecture notes; having to take notes on the fly was hard as I was sometimes watching the lectures on the train or during dialysis (one arm is disabled).
Is it really necessary to use up so much of the screen when showing the videos with the logo of deeplearning.ai?
Just a comment on one important shortcoming of online instruction: As a professor who teaches statistics, it is interesting to see the loss in learning that the student experiences through the absences of individualized feedback. One learns way more when one can talk to the teacher(s), and I guess this high volume throughput style of teaching limits what can be taught online.
por Omar A•
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.
por Alessandro P•
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.
por Trevor M•
info is really good, but there's a lot of handholding in the assignments where it matters, but also, no help afterwards,
Assignments might as well be a follow-along, one-day seminar, as opposed to a bonafide challenging assignment. I can only hope that the latter assignments get better as the material become more challenging.
I loved the assignments for the Machine Learning course with Andrew Ng (with Matlab), but these assignments are far too trivial, and are essentially just "fill in the blank". Perhaps, given that I've already taken that course, I should be looking for something more challenging than this course. Lectures, on the other hand are very good.
por Volodymyr B•
Assignments are too easy. Too little work to do for yourself. And explanations build into assignments are quite distracting. Also I would like to see more built in questions at video end. It's really cool motivation, when you know you should remember what is being said to use just after. I'm gonna take the second course but I'm somewhat disappointed :(
por Lucian F•
Excellent material, but there was a bit too much hand-holding on the programming side: not challenging enough on conceptually figuring out stuff (just the hassle of working through someone else's code).
por veit s•
Programming assignments are too easy, mostly copy and paste.
por Tracy B•
The notation used in the course was horrible and correct math notation should be used even if the course is not intended for math students.
I also feel this course should not be labeled as intermediate skill level. This was a very beginner level course. I have a PhD in applied math and was simply looking for knowledge in deep learning since my doctoral work was in a different field. It was very clear that I am WAY behind the target audience of this course. That's not necessarily a negative reflection on the course, but I still didn't find it very useful and feel like it should be labeled as a beginner level course.
por Jerome B•
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.
por Muhammad A•
Great attempt but it failed to provide complete details. Specifically the project files and their loading mechanism
por Francis J•
too easy, suitable as an entry level class
por Doğukan L•
I did the ML course from Andrew Ng before and it was amazing, which is why this course was so disappointing. It should've been named "Casual Deep Learning" rather than "Deep Learning Specialization"
Programming assignments were ridiculous, they literally had the answers on the notebook you're working with. On top of that the grader doesn't work properly either, so what's the point even?
I had prior knowledge about deep learning but the course was so repetitive that I feel like it would even bore a beginner. Andrew Ng talked about the same matrix multiplication and derivation processes over and over again and how important they are, while at the same time reassuring students that it wasn't a big deal if they didn't know calculus which I strongly disagree... If anyone wants to learn deep learning they should at least understand _the basics_ of calculus, linear algebra, probability and statistics. I understand this is an online course and level of entry isn't very high as there are many people from various backgrounds trying to break into the industry but still I feel like downplaying the importance of a good mathematical foundation is giving people false hope.
por Marc W•
Wow, Ng's lectures are really good, though challenging. The labs - horrible. Really wanted to apply the theory, but they just throw you under the bus on programming.
No examples, just program. Kind of like a really good lecture in English on Russian history and culture, and then:
"ask for directions to a car park in Moscow near the Kremlin"
Speak Russian here:
"aldksjflkajs lkasjdhflakj "
Wrong, try again
Seriously, they don't even clearly communicate what they want you to program.
por Zaur Q•
I think overall course if very bad and discouraging. There is almost no connection between video lessons and programmer assignments. Instead of writing so much formulas during lesson tutor could spend time on explaining some part of code (it's very difficult to understand tasks only from decription). During the second week Tutor explained little bit code. But then there was no more connection between videos and assignments. Overall I'm very disappointed
por Domagoj K•
I am very disappointed with this new course concept where you have to pay 43$ a month to be able to solve a quiz. Coursera used to be famous for its free courses and now it just removes free features over the time. It has become another site with expensive courses. I watched first week lectures and this is probably my last time to enroll in Coursera course.
por Manish S•
This course is more of spoon feeding, I liked the introduction to neural network in "Introduction to Machine learning" course better.
por Maxence A•
The programmation exercice are nice, but the courses are mainly about very basic linear algebra.
por Joseph K•
It will be a good course when you dump jupyter note books.