Excellent intro into Neural Networks! Starts from zero, outlining enough of important details, but not too much. Provides some good intuition. Lecture are divided into digestible chunks. Programming exercises are fun. It's been a very enjoyable learning experience!

I've been trying to understand the basic concepts of neural networks and know more about the fuss around Deep learning. Didn't quite find the right answers to I took this course. Aptly delivered with necessary support system to walk through the jargon of the field.

It was a very good introductory course. I had already studied the Machine Learning course and in this course a bit overview but thorough stuff about neural networks was taught. Also, the programming assignments were good. Overall, it was a good learning experience.

This course gives us a clear understanding about Neural Network and Deep Learning itself. We can follow this course without any fear of missing out of the subject due to Andrew Ng and all the facilitators give a very comprehensive knowledge about everything. Thanks

This is the best Online Course I have ever done. Sir's teaching was very good. This course gave me a very good understanding of how neural network works. I have also attended Sir's "Machine Learning - Stanford University" course and it was also an excellent course.

I would like to recommend this course to the professionals,students and teachers who are interested in AI or sub-parts of it. It is a very interesting course and easy to understand.I would also like to suggest this course prior to Machine Learning course.Good luck!

I must stay, one of the best course to start with if you are looking forward to get in-depth understanding of how neural network works. Thanks to Andrew Ng for such composite compilation of concepts of Linear Algebra and calculus... Looking forward to more courses.

The Professor was very helpful in making sure even beginners understood all concepts. The process of defining helper functions and then calling them inside the NN model is a very step-by-step incremental way of doing things which makes it easier to identify errors.

I have 2 master's degrees that explored AI/ML applications and I still found this class interesting and informative. The lectures clearly explained the concepts and the homework illustrated them without getting bogged down in coding minutiae. A great intro course.

One of the greatest lectures on the planet! I was board with one thing: programming assignments are way to easy. I think it would be great if there were just description of a method you need to build, with named input and output attributes, without any code at all.

This is it. This is the best course on Deep Learning that exists today. The lectures are excellent, but the assignments are truly the work of a master. Perfect construction of small assignments that finally come together to build the complex tools of deep learning.

Very good introduction! Covers basics and lets you try a bit for yourself. I kind of wish the assignments were a little less "guided" - but then again, it gives you an introduction to keeping your code structurized as well. (And I can always experiment on my own ;)

Andrew emphasizes a "deep" understanding of how it works. You get a firm theoretical base. A good number of mini-projects makes you technically fit. Very nice interviews with Icons of the AI: Geoffre Hinton, Pieter Abbeel, Ian Goodfellow, ... Can only recommend

Great introduction to deep learning. It includes the necessary linear algebra overview to understand the inner workings of deep learning, yet it is clear and concise. The flow of the course feels natural and everything is made so you can progress and learn easily.

I think this is one of the best courses for the beginners in the field of Machine learning, deep learning particular. The way of teaching is very good and the way programming exercises are designed teach you a lot about basics of implementing deep neural networks.

I have learnt a lot of things related to DNN in this course. Highly recommended course even if you are new to deep learning and machine learning. Course content addresses every basic aspect of neural networks and the tips provided by the professor is very useful.

I really liked Andrew Ng's videos. If I could perhaps point some weakness of the course it would be the lack of mathematical emphasis. However, if you are curious and committed to learn, you can go deeper into the mathematics of deep learning -- sorry for the pun.

Clear explanation, interview with experts to understand their journey in Deep learning, Programming exercises are all the best things of this course. Helped me understand the concept and implement them. I plan to use this learning to implement few of my own ideas.

Great overview from Dr. Ng. He does a great job of making the math easily digestable and helping students understand how it relates to machine learning. Very helpful to start at logistic regression and move into deep learning to help paint a more holistic picture.

Lovely course. It explains things rigorously enough, without overburdening the students. The programming assignments not only contributed towards my understanding of machine learning, but some effective coding practices as well. I would recommend this to everyone.

Great course ! Pratical and goes deep into the concepts. The exercises are in pythons notebooks, that I prefer most than Octave in the other courses. Loved the interviews. This course is a must do for those interested in understanding and practicing deep learning.

It is a wonderfull course. I really recommend this course to people who want to start a career in Artificial Intelligence. Course instructor has good knowledge and experience in this field. I also liked program assignments which help you practice what you learned.

Mister Andrew Ng is FABULOUS!!! The course is marvellous, the way he explains, the content, the organization of the different topics, the increasing difficulty...

I can't find words to express my admiration to Mr. Ng.

Thank you very much to the Coursera equipment!!!

Really in-depth understanding of Neural Networks. I appreciated how it goes into extreme detail on how the multitude of training data and parameters maps into Vectorized code. The intuition and detail on backpropagation and gradient descent is also very thorough.

To those that say that too little linear algebra / derivation is done, I invite you to go look them up - the internet is a wonderful place, and all derivations are readily available. I do however wish the audio quality was a bit better. But that is insignificant.