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.

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 :(

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).

Programming assignments are too easy, mostly copy and paste.

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.

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.

Great attempt but it failed to provide complete details. Specifically the project files and their loading mechanism

too easy, suitable as an entry level class

My main motivation to take this course, is to have constructive feedback on what parts of my code implementations were wrong and if so, why is it wrong. I realized that the grading is mere mechanical and doesn't offer useful feedback. Without proper feedback and right explanation, I would never be able to learn the concepts 100%, which defies the purpose of me signing up for this course in the first place. I don't mind paying even 100 to 200% more than what I'm paying now, but if I can't be taught nitty gritties of the models, then I don't feel like it's worth my time.

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.

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.

This course is more of spoon feeding, I liked the introduction to neural network in "Introduction to Machine learning" course better.

The programmation exercice are nice, but the courses are mainly about very basic linear algebra.

It will be a good course when you dump jupyter note books.

giving low grade for ongoing delays of course 5

视频都播放不了，反馈了也没个结果，怎么学？

N/A

Hello Andrew Ng Sir & Coursera Team,

Tell your instructors about yourself.

My name is Amit Wadhe. I am software engineer working in Walmart, Bangalore, India. I have 4 Years of working experience. Prior to Walmart I was working for Morgan Stanley. I have done my Bachelor of Technology in Computer Science and Engineering. I was always passionate about the computer from my school days. Out of curiosity I did my first C Language class in 10th Standard(School). That too with daily up-down of total 180km with train for one month from my hometown to Akola city. That time there was no computer courses offered in my hometown. After my schooling, I decided to go for engineering in Computer branch. I think that is enough in short about me.

Why did you take the course? How has it helped you?

I am working mainly on Java applications for last 4 years professionally. In last couple of years I realised that its not something which is exciting me, Its not something I wanted to work on. I was not sure what I wanted to work on, what excites me. I was hearing bits and pieces about Machine Learning and Artificial Intelligence since long from friends and colleagues. I was having perception about AI is that it's something big, something rocket science, something not for normal professional. But I got true trigger when I saw video about self driving car in silicon valley. That time I felt, Yes I wanted work on something like this, something which can be useful in real life, day to day life. I started searching about ML courses on google, I saw multiple courses on Udemy and Coursera. I red feedback about some courses. First place I started with some Udemy courses on ML for beginners but It comprised of only on how to code instead how it work internally. I was interested in knowing how something works internally instead of more in coding part. As I was Java developer I knew coding is not big deal. So I was curios about how ML models work internally, what is mathematics behind it, I was having interest in mathematics from the school days, though I did not score top. Then I started with ML by Andrew Ng on Coursera. After completing course, I felt like Yes, this is what I was looking for. Post completion my curiosity in deep learning has taken deep dive and I started looking for more courses by Andrew Ng on Deep Learning.

This course helped me to clear my understanding about how Neural network works mathematically. I was knowing bits and pieces about neural network steps like forward propagation and backward propagation but that was partial knowledge. After completing course I got that satiate feeling, Yes I know now, I understand it now in and all.

What did you love about the course? Tell them!

"I loved the bottom up approach of Andrew Ng Sir explaining concepts and Unveiling the treasure".

Irrespective of background I think anyone can understand the course with some knowledge on matrices and linear algebra. Recalling required knowledge learned in previous slides in short before diving into concept. Pace of course is also something which helps to grasp concept easily. Very intuitive examples helps to understand concepts faster. The example which I like most is about Neural network model of housing price prediction where Andrew Sir told intuition of hidden layers which is really connected to real life examples.

This course was insanely clear and meticulously constructed. As someone who does data science work professionally, I so appreciated the thought that went into the design of the videos and the programming assignments. You are seeing really exemplary code and also really sophisticated use of the Jupyter notebook! Also, the test cases are so well-constructed. You really get to *see* all of this stuff working or not with the carefully designed helper functions that allow you to visualize the decision boundaries and view training examples. Of course, the writing of these helper functions is no small feat. IT WILL NOT BE LIKE THIS WHEN YOU CAST OUT ON YOUR OWN. But, what this course does for folks (like me) who didn't have the benefit of a course like this in their formal schooling (perhaps they are too old and this stuff only got well-organized and codified more recently) is provide exemplars. Will your code always look like this for everything you build? No. But it shows you, using the exact technology that you are likely to employ professionally (tensorflow is coming up in the next course), what is possible. I look forward to rest of the specialization.

A note on the pacing: Perhaps because I am already very familiar with python, numpy, and Jupyter notebooks, I was able to complete this course in about two days (rather less than 4 weeks). However, I still got a ton out of it. I think it is paced the way it is so as to be viewed as more accessible by everyone, and also not with the assumption that you want to dedicate the majority of a weekend to it. Probably also there is something to the psychology of completing it so very ahead of schedule that the designers of this specialization are not altogether unaware of. But, if you, like me, know that you want a refresher on neural nets that is going to be practical and useful, in that it will help you both implement them AND understand what you're doing, this is a quick and effective way to jump back in.

Finally, since this is such a quick course, I really recommend NOT skipping it, even if you want to get to the more advanced topics in the rest of the specialization quickly. The course is so thoughtfully designed and concepts are introduced in a very specific and intentional way to make sure you understand each step before the course progresses. Based on having experienced this careful design, I expect the notational and programming conventions established in this course will make the next courses in the specialization more accessible.

In conclusion, this is I think the best online course with integrated programming exercises I've ever taken. I think it might be a standard-bearer for the whole field. Well done!

Copy&Paste from the survey I wrote earlier.

In 2012, I graduated with a statistics degree (BS) from the middle west where many companies hire data scientists to do simple analytics work. With my dream to do more predictive modeling work, I decided to go to the west coast and join the University of Washington to learn statistics in the master's program. One reason was that UW offered a great statistics program that most students chose to continue the Ph.D. program. The other reason was that Seattle had a few great high tech companies for me to explore opportunities at. However, although the MS program gave me a strong background in statistics theory, I found the industry moved so fast that my knowledge was falling behind the industry needs. In 2013-2014, I took Andrew's ML course on Youtube and Amazon hired me as a data scientist in the marketing department of Cloud Computing department (AWS). I figured that as a stats major I didn't have the knowledge in cloud computing or marketing, so in 2015 I took Coursera's big data specialization offered by UC San Diego, and the digital marketing specialization from UIUC. Later, I found another ML job at Amazon, using a lot of big data tools (Hadoop, spark, etc.) on AWS. After a year of settling down in San Francisco, this year, I decided to pick up the knowledge in deep learning. The first course of DL was fundamental but contained so much information that sometimes I needed to review several times because I forgot many statistical theories back in school. I thought it'd be very hard course but Andrew did a great job designing the curriculum where the theory and the application have a great balance for working people like me to start with. The amount of homework was much easier than I anticipated. I think for students who want to take the real challenge of coding, should hide Andrew's hint and write own functions. Overall, I like the Coursera courses and will continue to learn.

This course is great! I wish they would release a new version of the course where the math is visually explained instead of just handwriting by Dr. Ng. I think having to work with a small tablet really hapered his ability to develop the ideas as he was always trying to pack a lot of information on one ipad screen I would think that he could just stand in front of a white board and write on it with maybe hiring a sound technician this time? because despite the really high quality content of this course the audio is terrible and with the ipad screen not really doing justice to the writing, it really takes multiple viewings to figure out what's going on.

I would also suggest that Dr Ng really should explain when which one is which when he is using Y vs y and X vs x ... I'm sure it's crystal clear in his mind but for newbies like me, it can be confusing at times when there they write x but mean X (and vice versa)...

I still think this course is brilliant and it really cleared many concepts in my mind. It answered a lot of questions I've had after watching the fast.ai course. So if you're doing the fast.ai courses, you should definitely at least audit the deep learning.ai specialization courses and tbh, $50/mo is a steal for the calibre of information that is on offer (video/audio and ipad issues notwithstanding )

Work through it and you will find it extremely rewarding! Don't give up, keep going and if you feel frustrated, take a break and rewatch the videos the next day after a good night's sleep. It really helped me that I watched and rewatched video lectures, did the quiz, failed and came back to understand why I couldn't answer quiz answers. Good luck to all and Thank you to Dr Ng for making this available to us free of charge (if we wish to audit) I would buy the specialization though, since it is worth every penny and then some!

Good introduction to implementing shallow and deep neural networks in Python. If you have no knowledge of neural networks or Python, I'd suggest doing a little preparatory study first so that you know what a neural network is and feel comfortable writing short Python programs.

Theory: the course is not heavy on machine learning theory. I had covered the theoretical parts previously in other courses. This course provided a useful summary of these and left me feeling confident that I had a good overview.

Maths: this course doesn't place great emphasis on the mathematics. It shows you the relevant equations, with the emphasis on understanding the underlying concepts rather than going through detailed derivations. Sometimes there's an optional extra video going through the equations in a little more depth. A frequent message is: don't worry if you don't understand all the mathematical detail, you can still learn to implement neural networks effectively.

Implementation: the course uses the Python NumPy library throughout. It does not go into deep learning frameworks such as TensorFlow or PyTorch. From the outset, you are taught to use NumPy in an efficient ("vectorized") way. The programming exercises are well thought through and I found that they all worked smoothly, a pleasant change from some other courses elsewhere.

Overall I found this to be a gentle but satisfying introductory course to the Deep Learning specialisation. Andrew Ng is an excellent teacher. His manner is both calm and enthusiastic and he clearly cares about equipping students with the skills that they need and doing so in an accessible way. The optional "Heroes of Deep Learning" interviews were particularly interesting, full of gems and hints about what could lie ahead if you decide to go more deeply into the field.

TL;DR: Very much worth taking if you're looking to get into the field, develop (much) deeper understanding of the underlying theory and the necessary infrastructure.

I first gave it 4 stars and then changed to 5, let me tell you why. If you're reading this review, you are most likely considering taking this course and you very likely have some idea about what Deep Learning is supposed to be. You're also probably aware of the "black magic" stigma surrounding the field and that it is going to take some time to get used to the way of thinking, even though if you have some experience in "conventional" machine learning. Well this course (read: it's creators) also understands all of those points extremely well. With that in mind, the course caters to people who are are making their first steps in the field of DL, people who are not expected to have a high degree of expertise in dealing with DL models and especially not in creating those. Students are expected to understand about 85% of the underlying theory in order to get the models working (the rest is mostly calculus needed for deriving certain more difficult gradients) and the coding assignments include a considerable amount of hand-holding. That fact made me want to say how the course was trivialized in a certain way, and it really is (but don't let this discourage you; you will still need to implement all of the key parts and do take your time to really understand what they do), but then I thought about that again and concluded that I most likely would have struggled to complete the course otherwise. Andrew Ng and the deeplearning.ai team had a wonderful approach to teaching this course, it kept me coming for more and I cannot wait to start with following courses in the specialization.

I have recently completed the NLP specialization and decided to get a good introduction to the fundaments of deep learning. I was very satisfied with the NLP courses. Therefore, I chose to do the DL specialization as well. Andrew Ng is an amazing educator. The material is very well composed and thought through. I had already studied DL from diverse sources and I must say, the formalism presented here in this course is to date by far the best one I have seen. As a mathematician, I know that notation is power. Good notation will save you a lot of time and help you quickly understand and generalize concepts. Andrew Ng knows that also and put a lot of effort making the whole course very precise and without any loose end. This makes the learning very rewarding and easy to follow. The combination of mathematical formalism and intuition is on point and will help learners with a more programming background and the ones with a more math background as well. This is usually hard to achieve, the correct balance between practice and conceptual definitions. Everyone entering the field should be introduced with this choice of formalism and presentation. Congratulations on developing this notation! My only critique that I must make is the choice of the notation about the partial derivatives of the cost function. I pledge to reintegrate dL/dW into the notation, because this makes it clear with respect to what we are taking derivatives and also easier to get your mind around the chain rule. I hope this will be integrated in a new version of the course. But again, this is a small detail in the big picture. Awesome work! Thanks for providing this to the public at a such a price tag.