Course is nicely constructed. If you have 2-3 days without other commitments (I didn't) you can finish it very fast including all the - non-required - computations on paper. Coding excersises are well designed although not very demanding. Additional, more complex (bonus) excercises would for a nice add-on to the main course.

The only problem I have with the course is that I must wait 4 weeks for the next step, despite finishing the first stage during the first week. I do not understand why.

This is the first Course ever that i took on Coursera and i loved it so much. Andrew is an amazing Teacher and the Programming Assignment are extremely detailed in a way that i have never seen before, even if you didn't quit understand the concepts well in the videos, when you get to the assignments everything starts to unfold. THANK You so much to everyone who contributed in the making of this course and I'm looking forward to start the second Course and the courses of the Specialization.

I can not express the amazing professor Andrew is. He is capable to explain complex concepts in a way anyone could understand.

I would also like to say that the assignments of this course are amazing. They have taken a lot of time to create the Python notebooks and to match every single line of code with what was showed in the lectures. It's almost impossible not to get confident with Neural Networks after you have completed all of them.

My sincere congratulations to all the teaching staff.

Absolutely wonderful! I have strong math and CS background. I can see this course can be learned by many people from many background.

VERY helpful. I cannot say enough good things about it! Thank you so much!!

The only one thing is that it will be great if the homework assignment deadline can be even more flexible. I work full time and I have a big family. I have been working holidays and weekends to get the course done. It will be nice if the deadlines are more flexible for people like us.

Awesome course. The teaching style of writing down each small step made it easier for me to understand complicated concepts. Previously I had taken the ML course. Here, the course starts with a simple logistic regression as neural network then 2 layer NN and multiple layer NN. The assignment projects have clear explanation to guide through the assignment projects. The forums are a great help to find cause for errors etc. Thank you Prof. Ng. Looking forward to other courses in this series.

Excellent course! Concepts were explained very clearly and concisely. I really appreciated how much detail the instructor went into. A lot of courses and resources tend to brush over important concepts and just focus on practical applications. It helped a lot to learn the nitty-gritty details that make neural networks work. The assignments were reasonably easy and informative. Only possible improvement for me would be to have more detailed explanations of the calculus behind the networks.

A great introduction to machine learning using neural networks. This course provides a general overview of the mechanism of prediction and optimization of the prediction (gradient decent) using a neural network. The hands-on approach and the minimal lecture videos makes the course interesting.

(I think there might be a way to revolutionize the concept of Discussion Forums to make the learning more interactive with fellow learners but I don't know how so I don't blame the course creators!)

It's a very good course for beginers in machine learning. Every theoritical aspect of Neural network was explained brilliantly by instructor. Andrew is perfect in his job. But i was little bit disappointed in programming exercises because i don't know much about Python so i didn't understand some of its functions and mainly its imported libraries of Jupyter notebook. There should be one more class or at least a document to explain the libraries and function used in programming exercises.

Neural Networks and Deep Learning course has been a great learning experience, I had high level idea about how a Neural Network works, having used on structured data before through packages and libraries. But after completing this course and building a neural network from scratch using only numpy library, I now have a good understanding and intuition about why a neural network works. Kudos to Andrew Ng and his team for putting together the content and assignments. This course is helpful.

Another excellent course by Andrew Ng. His instruction style is detailed, without getting into the weeds. The lectures provide enough background so that people with (like me) and without detailed linear algebra backgrounds can understand how these algorithms work. There is also plenty of resources available for helping with programming and other details of implementing these strategies. Highly recommended if you want to learn more about neural networks and how to actually implement them.

This is a great course for AI and ML enthusiasts to start learning and making deep learning models. The explanation technique is really good, which makes learning it even more interesting and involved. The programming assignments are intuitive and involving. All in all, if you want to start out in Deep Learning, this is the place for you.

It doesn't matter if you don't know or want to learn the complicated mathematics involved in this field, because Andrew Ng has you covered for that :D

This is another excellent course from Andrew Ng.

After doing his Coursera Octave based initial Machine Learning course I thought I might get up to date to Python /Numpy techniques.

As usual it was excellent. Assignments were not too difficult and you can resubmit multiple times (both quizzes and programming assignments).

Being able to use iPython Notebooks is a great positive point. Installing Python3 + Tensorflow with GPU support took me a long while (just before starting the course).

It was really an amazing experience. In the vast world evolving with Data Science and Deep Learning. If such a course is available to build the basic foundation of deep learning, It is a matter of too much proud for the coursera as well as the legendary teachers like Andrew Ng who spent their a major part of their life in learning and research in this field and made it easy for the fellow learners. I have no words to express my gratitude towards this course, I had learnt a lot.

Thanks!

I love how this class continued from the original ML class and switched tools from MATLAB/Octave to Python, pretty effortlessly. I was a little worried at first because of the numpy ways of handling matrices and vectors compared to MATLAB/Octave, but in the end it wasn't that hard to switch gears... (I prefer the .* and ./ notation, but I suppose it's good to know both and not get too attached to one implementation for basically the same thing at a much lower cost point, i.e. FREE!).

I had little to no knowledge of Python and numpy and some matrix knowledge before this course, although I did implement Neural Networks years ago in Java with loops but without matrices. This course was great in helping me learn enough Python, numpy and matrix math to actually write a functional Neural Network. The videos were very informative and fairly easy to follow, although it helps to see them twice before the tests. Take some screen shots of the white board for later reference.

This course breaks down deep learning concepts into small enough to digest pieces. It's a very well-structured course and it takes away the fear of calculus and matric calculations. What I liked the most about this course, besides Prof. Andrew Ng, is it gives explanation for the correct answers to practice questions, which not all the courses do. The assignments are very well put together with lots of self-help remarks. I recommend this course to everyone, even the non-mathy learners.

Andrew Ng is an amazing instructor! This is the best explanation of deep learning and neural networks that I have come across. I love how he explains everything from the basics and still covers so much ground. I was intimidated by backward propagation before taking this course. But after going through this course, it seems it is just a fancy name for the chain rule I learned in high school calculus.

Andrew's dedication to helping others learn shows in every lecture. He is a rock star!

Very complex concepts have been taught without assuming much mathematical background. Andrew is an extraordinary teacher. I aspire to become as good a teacher as him one day. Also the programming assignments are well designed. The Jupyter notebooks are extremely detailed and are very easy to follow. The interviews with the creators and developers of the field at the end of each week's material are invaluable. Over all, this course and the others in this specialization are outstanding.

Sincere congratulations to course content creators and specially Andrew Ng. My journey of this first foundational course has been amazing which has left me with the both theoretical (maths part) + implementation from scratch. Just Amazing. I have never been able to gain earlier such a beautiful initution behind Derivatives/gradients. Similarly the way course illustrates of one by one removing loops and build a system of vectorized implementation has been remarkable. Thanks once again

A superb mathematical introduction to deep learning. Professor Ng and his team ensure that students gain a solid foundation in, and intuition for, the subject.

In my opinion, students will find they are able to focus on the deep-learning algorithms a little more clearly if they are already comfortable with linear algebra and basic Python. Therefore I would recommend taking a course in Python and having a look at basic linear algebra in preparation for this course or in parallel to it.

Definitely the best introductory course I saw about Deep Learning! :) Andrew is a great 👨🏫 lecturer, always emphasising on the theoretical and practical concepts, developing first the right intuition about the topics to further tackle them with the formal approach and exercises in Python. Great methodology as well :) from the simpler to the more complex: it is almost impossible to be lost in this course!:) Thanks a lot Andrew and all the super team of this specialization!!

Very enlightening introduction to the foundations of neural networks. The course presents a lot of the math without being too exhaustive, and there is a clear link between the theoretical classes and the practical labs. The professor is very enthusiastic about the subject and structured the course in a way that all the questions I had left in one lecture were discussed in the following lecture. Very well structured course, would recommend it to anyone trying to learn deep learning.

Amazing introductive course! I'm a Data Science student who already had basic of ML and DL, but in this course everything was explained in an easy way and thus accessible to everyone. The end-of-the-week exercises were also nice.

I don't know if it will be introduced later on in the Specialization, but maybe directly using PyTorch or Tensorflow would help to increase the level of preparation in view of a "real-life job". But for a beginner this might add some unnecessary complexity.

Very good introduction to machine learning. The basic mathematical foundation is taught and we actually implement a NN from scratch with Numpy. In other courses I made backprop was just rushed, here we have the opportunity of see what is going on and then proceed to abstract it away and use the automatic mechanisms different frameworks offer. Thanks very much Andrew and all the assistant professor for the very 'deep' and well explained coverage of the NN during this specialization.

This class is a great overview of NNs. I have experience programming NNs using TensorFlow, which I learned how to do by following tutorials and using others' open source code. For me, completing this course really fleshed out my understanding and intuition of the internal workings of a neural network. Solutions for the programming assignments are mostly copy-paste-style, but one must think about the equations to do it correctly. Thank you Andrew, and the others behind this course.