The course is very, very entertaining. I enjoyed it. I liked your style of presentation of the material and its selection. To be more concrete, that's what I liked most:

+ Practical Assignments

+ Your explanation of hard topics in easy way

+ Deep Learning Legends interviews

Andrew Ng, thank you for the interesting, informative course! I can't stop watching your exciting lectures and solving interesting tasks and quizes. And now I am going to continue my Deep Learning Specialization!

Very well paced and great in terms of digestibility of the course material. The first course, given you have no issues with the Python syntactics, will help lay the foundation to the principles of deep learning. The bonus of every week is an interview with the stars of deep learning and neural networks. I am lucky I took the course several months after it's been commenced. So there are no errors and it's been a very smooth experience. Looking forward to starting the second course.

i didn't expect an online course to be so well made. i'm an automation engineering student, so i'm interested in detailed maths explanations, and the level of detail of this course is very good. although i would have preferred some more details on some arguments and calculations, that i had to research and compute myself, i understand that this course is not intended strictly for people with strong mathematical background. Congrats to the developers of this course and thank you!!

I tried many other courses but due to the level of mathematics in their i felt like i couldn't do it.This course has helped me a lot for getting the basic ideas of nueral networks and deep learning and the level of mathematics was also a lot better than other courses...Even if you have doubts while doing the course the 4th module will clear it all in the last...So i would suggest to all the people who are thinking to take the first step towards deep learning....Really loved it...

Best and best before this course I don't know about how these complex computation and how computer can compute all the derivative but now with Grace of Allah and this Course I am now satisfied with this course.. This coursers team and Andrew Ng for giving me this opportunity to become a data and machine engineer and know more about machine and AI and Deep learning. I am thank full to you all. God Bless you keep this charity work for poor students who cannot afford these courses.

Teaches deep learning and neural networks foundations fundamentally and practically very efficiently, quite concisely. Notation standard a little busy but I think optimum. Only thing was the contradicting matrix representations of W and X from lecture notes to the Python notebook medium: Sometimes X has m rows and sometimes it has n_x rows, and sometimes W becomes its transpose, even in the vectorized for all data points cases. Though, in the end, it helps one pay more attention.

Great course. Well taught by Andrew Ng. All you need as prerequisite is a little understanding of Matrix Multiplication, derivatives, specially the chain rule and a little programming experience. If one wants to understand things clearly, I suggest not to miss the optional videos. The interviews with other leaders in the field were informative as well except Geoffrey Hinton's interview which sounded a little high level for a beginner like me. I recommend this course to everyone.

I appreciate the work put in making this course so accessile. I loved seeing the equations and math done y Andrew Sir with hand. It helped me pause and do it myself and generate an interest in doing math and linear algera again. Thank you very much. The quiz qns are though not too hard however tricky enough. I liked the course structure too. I do think it would e helpful if you mention pre requisite courses in a specialization or if it's not required. Great course, recommend it.

The quality of the videos could be improved, but the quality of the explanations is excellent. I already knew many of the concepts introduced, but I really appreciate the detailed explanations by the instructor, and the tips acquired from his experience. The evaluation tests are OK, although a bit short to be honest. Practical knowledge is enforced by means of well constructed and very detailed exercises. All in all this is a great course for beginners, I strongly recommend it.

This is an excellent course. I have read tons of the tutorials of deep learning on the other sites. But only this course gives detailed explanation of all the steps. Of course with notebook style step-by-step programming, and Professor Ng's gracious lecture, one will find this course is definitely one of the best Deep Learning Courses available these days. I also very much like Professor Ng's practical suggestions for how to apply Deep Learning principles for real applications.

It was great understanding how programs can learn to do 'simple' tasks for humans, the steps and models and how they perform comparatively.

In this specific example, on interpreting images it seems however bizarre that a program needs thousands of trials and hundreds of images to classify the image. This seems much more easy to learn for a person.

Thanks for the course, it is great to have reached a basic understanding of it!

It was tough to use/learn vectorization in python... :)

Andrew Ng as usual is superb at teaching this course. Providing intuitive explanations, which for this topic is super helpful. The programming assignments are good but the implementation of the last NN assignment uses a slightly different model than what Andrew Ng uses in his slides, which makes things really confusing at first. It would help if the person implementing the code for testing adds a note saying how the nn model is different. That would save people countless hours.

A very good course for beginners hoping to get into deep learning. Professor Andrew NG makes Deep Learning theorems easy to understand and gives easy to understand examples where the theorems apply. Moreover, the graded programming assignments and quizzes help to solidify understanding of the knowledge gathered through the video lectures. The forums are there for anyone who gets lost or confused. Highly recommended for anyone whose interested to get started with Deep Learning.

After finishing first course from Andrew, I've found this one much simpler to understand, especially the back propagation; This might be because this course was solely oriented to neural networks (leaving behind linear regression or unlabeled learning algorithms), and that on the previous course I've already had to rationalize the back prop process.

Anyway, the explanations are much clearer on this course, the only thing I miss is the Errata section, tutorials and week's notes.

I'm so grateful to Mr. Andrew Ng and all developers and staff in this course for providing such a great Deep Learning content. His teaching is so careful and easy to understand for the difficult topic. I also watched the videos of his interview with famous AI developer in the world and they were really helpful for me to get a better insight into AI, what is necessary and unnecessary components to be successful in this field. I'll keep following all the rest of the courses.

very useful course, key slides for me were the matrix shapes in prop equation walkthrough and forward/back prop equation summary. For me, using real life input (the cat images) was key to build a mental model to check and process understanding. I also found explaining the completed final assignment to someone else was useful in checking my own understanding - ended up using the cache in the final assignment to print X, W and Z matrix shapes to talk through the vectorisation!

The course is clear and to the point. Even though the Neural Networks is a tough topic to understand, Andrew is very good at explicating it in simple terms. Assignments could be a lot more lengthy, what I meant was rather than just changing the simple lines of code. Design the entire function could make the assignment a bit hard and also make us understand the problems we will face while writing the code. And also how to write the entire Neural Networks algorithm on our own.

This is an excellent introductory course for artificial neural networks. The programming assignments are very helpful for solidifying the knowledge gathered from the video. I love the fact that most of the code is already done as boilerplate code and the learner gets to implement just the part of code that requires his/her concept of deep/machine learning. This way learner can focus on building his expertise in deep learning without being an expert in programming beforehand.

This was again an excellent course on the basics of how to deal with building a L-Layer MLP or NN. Working in python and numpy in Jupyter really helped. Solving the mechanics of the problem, especially in regards to tricks of matrix, vector sizes, rank arrays and piece by piece model building methodology really helped. I am ever thankful to Coursera , Dr. Ng and the fantastic team. Just a suggestion: make the programming exercise a little harder (like the original ML course)

This course teaches you the deep learning and neural network algorithms from the scratch of mathematics. You can actually get the intuition of the algorithms and understand how it is working. Also, the assignments are very helpful to create understanding and practicing the lessons learned in the theory part. I am really impressed by Dr. Andrew Ng's way of teaching and explaining one of the most complex parts of Calculus and Mathematics involved in Deep Learning. Thank you !!

Absolutely the best presentation of neural networks I've heard. The way Andrew Ng organizes the material, thoroughly giving intuitions and building up concepts from simple single case implementation to vectorized implementation gives you the confidence to tackle the course from start to end. The most annoying thing for me were the little bugs with the coding assignments that resulted in an assignment being marked as 0 despite running and yielding the same results as written.

Great lectures put together by Andrew and team. Also found the programming exercises to be useful and informative using the Jupyter notebooks.

Also, as someone working a full-time job, I really appreciate the balance between breadth and depth. The coursera team has made it very easy for me to pop open the courses for maybe an hour a day and continue where I left off at another time. The ease and simplicity are one of the key reasons I'm able to continue taking these courses.

A fabulous introduction to Deep Learning. What amount of thought that has gone into building this course is evident. The programming exercises were easy, but that's not a bad thing. I don't want to get demoralized by an exceptionally tough programming exercise right up-front. They were easy, but not trivial. The code blocks were exceptionally well thought out and the notebooks had all the info you needed. They were also directly related to the material taught in the videos.

An EE (with experience in the field of ASIC and electronic systems design). Wanting to get hold of the field of Deep Learning due to its tremendous potential in about every field and a recent surge of activities in ASIC designs for Deep Learning.

I did Andrew's Machine learning course before this (and I feel that ML course is a must as prerequisite to this) and found this course very good in all aspects (material, quality of presentation, quizzes and programming assignments.

Great class for learning the basics without delving into quite as much of the linear algebra and theory. There is still plenty of the fundamental information shared and used throughout the labs.

By the end of the course, I had a firm understanding of how forward and backward propagation worked, how to set up the appropriate calculations through numpy, and how to debug when things went wrong.

Very much recommended, especially as step one of the Deep Learning specialization.