Jan 18, 2020
Very structured approach to developing a neural network which I believe I can use as foundation for any project regardless its complexity. Thanks professor Andrew Ng and the team for their dedication.
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 :)
von Aditya C•
Aug 05, 2018
This course is second to none. Nevertheless, I feel that too many implementation details are given in the course videos which could have been replaced by strong mathematical analysis of the algorithms. Furthermore, the tests were very easy for an experienced programmer regardless of his/her expertise. This is partially because way too many hints were embedded in the Jupyter notebooks than what is necessary. A more stricter programming exercise, not in terms of the neural-network complexity, but in terms of hiding-hints would have made this course stellar. To recapitulate, while the course is exceptionally crafted, I felt that stating my opinion would leave room for improvement. More concretely, in machine learning jargon, we must not be content with a local maxima thinking that it is a global maxima. Cheers!
von Paul F•
Jan 28, 2018
A very well structured introduction to the basic algorithms of multi-layered (deep) neural networks. I have not studied calculus, but the careful way Andrew Ng's videos help develop intuitions about the algorithms and the way he introduces notation made it possible for me to get a lot from the course. I've never programmed in python before and it was illuminating to see why its so popular in data science given the power of its math libraries. I wish I'd had as sympathetic a maths teacher as Andrew Ng when I struggled with it as a callow schoolboy. My background is that of an experimental psychologist and I was relieved to find that in the section "what has this all to do with the brain", that Andrew Ng gave a superb explanation that counters the hype so often found around this kind of AI. Great course.
von Alexandros S•
Apr 03, 2019
Excellent course to get started with Neural Nets. Also the first week is kind of an intro - so its basically 3 weeks.
Tip: If you are kind of clueless with Python ( as I am) you may struggle a little bit when it comes to deciding when to use vectorized solutions or not, loops and indices in arrays and all of that stuff. But don't be intimidated, my advice is the following: Go back to the lecture videos and rewatch them until you make sure you understand 200% what the exercises are asking for. Then implementation becomes easier as you at least have only the coding part left to figure out..... and that actually is on the easier side in this course.
Also do some basic wikipedia level reading on matrix multiplication - you'll need that for sure if you dont know linear algebra
have fun you....data scientist ;)
von Adrien W•
Oct 15, 2017
I bounced back between whether I should give this class 4 or 5 stars. On the one hand, you don't do a lot of coding. Initially, I thought this was odd given that the subject matter is so programming dependent. Most of the assignments are on rails, so to speak, with test cases after each function. I began the course wondering just how applicable the content of the course would be. But what you realize halfway through the course is that this is a highly complex subject and that the point of the course isn't mastery but instead familiarity. Now, having just completed the final assignment, the world of neural networks is completely blown open and it's very exciting. I highly recommend the course. It is like the karate of AI. Just do the kata and by the end you will unlock mystical neural networking powers.
von Mehran Z•
Nov 05, 2017
Having the passed the same course back in school, I found this one much easier to understand. I think Andrew NG is a brilliant teacher and thoroughly prepared. I wish my professor would have thought this course like this. Having said all that, I work as a software engineer and OOP is a must for me and I find it hard to follow how the programs were structured in the assignments. At the same time, I understand that this is a ML course and not a software design course. But I wish, at least, the assignment would have tried to develop the code from top to bottom (grand to detail) and not the other way around. In the current implementation, it is impossible to for to see the picture as a whole and I would just settle to implement what is asked of me instead of actually trying to understand what's going on.
Feb 06, 2018
I audited a similar course by Andrew Ng a couple of years prior to taking this course for credit. Both times, the course was very enlightening and was apparent that the course master and the mentors spend a lot of time discussing the content, making sure that the content can be (re)implemented, and refining the lectures.
The "heroes" interviews where also interesting. I hope that they can somehow assemble a course on reinforcement learning.
My current experience is that I have read several books and also read published papers on machine learning and worked with tensorflow for a while. Thus, I feel as though this was a wonderfully presented practical guide to building a DNN model that can eventually be tuned with greater flexibility than can some of the machine learning modules available.
von Joseph S•
Nov 13, 2017
Andrew does a great job of utilizing the online format to present complex material in a very logical, understandable sequence. Many MOOC classes I've taken will discuss concepts without introducing them properly. This course is very methodical and the concepts build on one another in an easy to follow pattern. I also appreciate building an understanding of the underlying concepts of Neural Networks before jumping into the frameworks like TensorFlow. I think it gives me a better understanding of what is going on behind the magic curtain. Finally, the coding exercises are the perfect blend of enhancing my understanding of the important concepts without getting bogged down in the intricacies of Python coding. It also gives me a good starter set of code for working on my own problems.
von Neeraj B•
Aug 22, 2019
This course is extremely helpful for beginners as well as people with experience. The course goes through a proper structure where first Andrew explains each concepts in detail in his lecture videos and each video covers 1 specific topic allowing you to process the material. This is extremely important if you're new to deep learning. Then there are practice questions to test your knowledge of the material covered in the lectures. Then you get programming assignments to actually implement what you have learned and not to mention for people with little python and calculus knowledge he even has some videos explaining basic python and derivative concepts related to neural networks and enough for completing the programming assignments. This course has been a wonderful refresher for me.
von Alex T•
Jul 21, 2018
A great introduction to deep learning. This course explores topics like binary classification, logistic regression, gradient descent, linear algebra in the context of neural networks, forward and backward propagation, computing cost/loss functions, the function/definition of parameters and hyperparameters in deep learning, coding classifiers in python using shallow and deep neural networks, general industry trends, and what misconceptions about deep learning exist in the media today.
+Python, Jupyter Notebooks, NumPy (and other packages)
I highly recommend this course, and it is do-able even for those without much coding or math experience. Thanks to the the team at deeplearning.ai for developing this course! I am looking forward to the following courses in this specialization!
von Pramod H•
Dec 12, 2017
A very well compiled course indeed. It has the signature style of teaching of Prof Andrew Ng where he dives into the concepts thoroughly without any compromise and bolster them through the coding exercise following it. The course focuses on the basic building blocks of neural network by taking away some of the burden of basic python syntax which is already pre-built and provided to you. That said such pre-built code is limited so i never felt that something major is left out. The programs are also built using smaller functions as building blocks. Some of the sections especially the week 4 exercise was a little longer and tougher but after spending some time to look at it for the second time helped understand it. Overall i have 5 stars with 2 thumbs up for this course.
von Hasan R•
Apr 24, 2018
Neural Networks and Deep Learning was a first ever course which I studied online, and after studying this course, It made my enough interest that want to take other courses as well. The thing which I liked most about this course was that it was beautifully structured. Andrew Ng explained the things in a way that I thought these concepts cannot be better explained. During lectures, Andrew Ng shared his experiences about writing python codes efficiently which helped me to complete the programming assignments in time. Most enjoyable part of this part was doing the programming assignments because every step was explained (what are we going to do and what we will achieve) and expected results were also shown to confirm our results before submitting the assignments.
von Daniel C K•
Aug 29, 2017
Great introduction to Deep Learning for those with no experience in the field. Guides you step by step through the exercises. If you've taken Andrew Ng's Machine Learning class, this course is mostly review with a few updates on Deep Learning notation and slightly more advanced vectorization for neural networks. The use of Python is nice, although Python doesn't come with vector manipulation built in like Matlab does. This leads to slightly more cryptic errors, but if you've used Python before, this shouldn't be problematic. In particular, the use of Jupyter notebooks makes for a clean interface, but debugging in the notebooks is more difficult compared to Matlab or Spyder. Overall an easy course to get you working in the Python Deep Learning environment.
von Christopher C•
Dec 02, 2017
Nicely eases someone with modest numerical Python experience into neural nets. Test-driven Jupyter notebooks (with the test data and tests themselves provided) made the programming exercises pretty easy, almost trivial. But that's how it should be--this course was really to introduce the concepts behind deep learning, and enough implementation so that students have an idea of how the tools they'll use work behind the scenes. Most of us will grab Keras-on-TF or something analogous and never mind the details, but this course nicely forces one to internalize at least some of how the sausage gets made. Andrew Ng is also a great lecturer, and his use of the presentation tools were masterful. The interviews with Names to Know were icing on the cake. No regrets!
von Mark M•
Oct 23, 2017
This was a great introduction of computing neuronal networks. As I came from the programmers site and my active math experience lies years behind it was a challenge to recap all the math behind the ML algorithms for me. But this is perhaps the major strength of this course to really make ist understandable. Honor for Prof. Ng his didactical concept. Also keeping track about the vectorized representation of the formulas together with careful elaboration of dimensionality following the forward and backward propagation chain helps to make the coding of the NN algorithm easy to handle. Think otherwise I would have wasted my energy in managing all the matrice and vector operations. Never thought that it is so easy to implement your own neuronal network class.
von Aaron H•
Sep 01, 2017
Good coverage of the basics of neural networks with hands-on exercises using numpy.
The notation is a little surprising -- most of the time we math people talk about dy/dx as being the derivative of y with respect to x. That is, when I wiggle x a little, what happens to y? The notation in this course assumes that everything is a derivative of the cost function with respect so something else, so the notation only includes the "something else". For example dW is the derivative of the cost function with respect the weights in the matrix W.
If you are not careful, it is easy to lose track of what dZ means.
If you are pretty comfortable with vector calculus, it moves pretty slowly at times. If your calculus is rusty, I think the speed is probably perfect.
von Jeff W•
Oct 08, 2017
While I'm good at perl, I wanted to learn python, and as I'm a learn-by-doing kind of person, I thought an ML course in Python would be a good place to start. I was surprised that "Deep Learning" was a bunch of the neural network techniques I'd played with in the past, and was a bit apprehensive about the amount of calculus that would be required.
This class breaks down the ML concepts quite simply, and helps you understand how to actually build and apply logistic regression, and then use that as a building block to deeper neural networks. They also give you an intuitive understanding of the mechanism and underlying math, without requiring endless pages of derivations.
I recommend this course to anyone looking to get a solid overview of ML techniques.
von Shehryar M K K•
Oct 02, 2017
This was my first foray into the field of deep learning. Dr. Andrew is an amazing instructor his humble demeanor made learning really enjoyable. I really like where he went into derivatives and did it step-by-step making me understand the math behind the scenes. The programming assignments were super easy only difficulty was my lack of practice with python. If I would have to improve on this course. I would say add articles for further readings with a short quiz after it related to the article. I would also like to take this opportunity to thank the coursera team who accepted my application for financial aid without which I would have never earned this certificate. Thank you for allowing me to learn something new and for making it easy and enjoyable.
von Tyler K•
Aug 19, 2017
Fantastic as always. I do wish it had a lot more math but I understand the challenge delivering that to a larger audience. My favourite aspect of Andrew Ng's classes is actually the absolute response by the grader system.
I learn very effectively in environments where I receive complete feedback on my problem submissions. Allowing me to correct my understanding of the material and retry. Contrast this with the PGM course where total scores are not returned and there are a limited number of submissions. I felt that my learning was stunted in that environment as there was no opportunity for me to correct my understanding of the material myself and have it re-scored.
Hopefully we'll see more math heavy classes in the future that retain this style :)
von Arpit S•
Dec 09, 2018
Finally, I had to sit down at a stretch and finish the course at a go! I think it was completely worth it and I thank coursera team for providing me financial aid to take this course. I am very grateful to have got this opportunity to learn from this excellent course. Will definitely complete all courses within the deep learning specialisation by a little at-a-stretch effort and i am sure it'll give a sweet boost to my understanding. The course material, Professor Andrew's way of explaining and the assignments are all incredible and i really enjoyed the modules for implementing back-prop the from-the-scratch way! Personally, I also feel the best way to take these courses is at a stretch which completely connects the dots for me. Thank you team :)
von Bryan H•
May 28, 2018
The programming assignments give you the hands-on experience you need to feel comfortable coding your own ANNs from scratch. Andrew's lectures are well-paced, easy to follow, and enjoyable.
Room for improvement:
The Jupyter notebooks, although convenient for the Python programming assignments, are unreliable. I spent 25 % of my time re-writing code because the notebooks wouldn't save, and I had to reload certain assignments multiple times, often at different times throughout the day. If you have made progress and the page doesn't save, then leave the tab open and copy-past your code into a new instance. Nonetheless, I can't fault the Instructors for the lack of fidelity in the intercommunication between Coursera's platform and Jupyter's notebooks.
von Liaw S W•
Sep 17, 2017
It was a great course, very well organized but after doing the programming assignments, I feel that I might not have fully grasped the concepts in lectures. The descriptions in the assignments were great and helpful, but I feel that the pace of the course was too quick, too easy. I feel that I must be missing out on something. Maybe it's because the teacher has done a very good job in explaining hard to understand concepts to the degree that they seem too easy to understand. Since this is only an introductory course in the series, it is understandable that it is supposed to be easier. Don't get me wrong, this course has very much substantial contents to it! Nonetheless, it was a great foundation course for the specialization! Thank you teacher!
von Noelle M V•
Aug 29, 2017
If you took Andrew Ng's original Machine Learning Coursera course in 2012 (as I did), you expect nothing less than an excellent course. Unlike Neural Network or Machine Learning courses at other learning sites, this one is far superior. If you are to ever going to fundamentally understand what is going on inside all those convenient Machine Learning and Neural Network software libraries and frameworks (versus just blindly using them), or perhaps build your own libraries; then you need this course. And, indeed, it is important to understand because not understanding removes all intuition as well as removes knowledge of boundary and limiting cases that you may encounter, which will make things harder for you. I highly recommend this course.
von Keely W•
Mar 12, 2018
I'm LOVING these classes!! The instructor, Andrew, is excellent, and the material is presented in a logical progression so that it's not too overwhelming. It definitely helps to have some background in math, namely Calculus and Linear Algebra. The programming assignments can be a bit tough if you don't truly understanding which Linear Algebra methods to use, i.e. dot product multiplication vs element-wise multiplication, but usually the instructions are good. However, I found myself having to look up a lot of Python and Linear Algebra basics online (Stack Overflow is your friend in this case.)
Definitely a challenging set of courses in the Deep Learning Specialization, but very well presented, and extremely interesting (at least to me.)
von Gary N•
Feb 29, 2020
This course allows you to quickly catch up to the fundamentals of building multi-layer NN models, by viewing it as stacks and layers of logistic regression units. You will sail through this course if you already know logistic regression. Even though nowadays most people don't even need to understand how the calculus actually works beyond a basic intuition, the calculus required for back propagation are well explained; detailed yet presented well for people with high school calculus to understand. The exercises are very simple with an objective not to test your ability to write code, but your understanding of how the steps are put together. The answers are practically given to you, you just have to put them together in the right way.
von Akhil C V•
Aug 09, 2018
This course is phenomenal. Even as someone who's spent almost a year working as a deep learning engineer, there were still many lectures I found incredibly useful. I believe the matrix dimension lecture will permanently change the way I structure the code for my neural networks in the future. If I had one criticism it'd be that it could perhaps get progressively harder. I love the ultimate task (of a logistic classifier), but as we go from week to week, I think there could have been less hints. Even by the end of the course, I felt like I was being spoon fed through the programming assignments. This is a problem, because I'm less confident than I would have been if I'd figured out the Lmodel forward propagation (for example) myself.