Excellent course... Andrew and your team really deep in the waters of Deep Learning and teach us in a extraordinary way... I love how communicate and guide through entire course.

Exercise and notebooks are really amazing.. and guidance has been key to complete the course.

Thank you so much to Andrew and his team !!.. Enjoy this course and i'll keep in next courses...

The content of the course is of very high quality. There is enough math to get by but deriving the equations have been left completely out and derivatives for example are just given. So overall very good for learning the concepts but doesn't include in-depth math. The exercises are on the easy side but for a beginners it's not a bad thing, they are still very useful.

Well, Now I am much more confident in Deep Learning. Guess what is the reason? It's this course. This course is magical in its true sense. Andrew Ng explains everything so well that even if we think that we will not understand the lesson, we will understand the lesson. Best course I have ever done on Coursera after Interactive Programming in Python by Rice University

Thanks to Andrew's detailed lecture notes and documentation in the homework, it is not hard to pick up the new concepts based on what I have learned about traditional machine learning and linear algebra. It is especially helpful for people that are a few news out of school to not having to worry too much deriving derivatives by hand all day long to get homework done!

It was an amazing experience to learn one of the growing course like Deep Learning from one of the Best Instructor in this field. I thank Professor Andrew NG and his team for this outstanding content. The Assignment in the course were well designed and it was easy to understand the complex implementations of the logic. Thank you Coursera for the wonderful platform.

It was a journey. From 0 to something like private *(it is still far "to hero"). Had very little knowledge in numpy, had to refresh a lot of mathematics from university days 15 years ago, but all guidance and explanation kept me glued to this course.

Expectation: create personal neural network from scratch after the course

Result: Should be possible - I think :)

Thanks

I understand every step, due to Andrew's teaching (really pedagogical!!!) and the programming exercises. This course makes me able to connect the "dots" between the theory part and the programming part. It is also nice that parts of the lecture videos can be saved as "notes". It would be nice, if lecture notes can be downloaded as .PDF files. 5/5 stars from here!!!

Straightforward and clear. If you took ML with Andrew before. This course will deepen your understanding of the forward and back prop algorithms providing you a very detailed and comprehensive structure of a basic multi-layer neural network. Not to mention that all the jupyternotebooks are good references that I guess I would even look back very often in the future.

the course has done a very well job in explaining the basic structure of a neural network along with the underlying math. The ipython notebooks were really helpful in understanding the coding aspect of the same. the overall goodness is really great. I'd suggest that inclusion of some animation or something visual can help understand the backpropagation much better.

This was a great start to the Deep Learning Nano Degree. I learnt the basic working of Neural Networks and also tried some examples on my own. This course is a huge helper to graduate students to me who wants to give a try to neural Networks. Great share of Learning about Deep Learning. Dr. Ng is simply amazing. One suggestion would be improving the grading methods.

This course opens the "black box" of neural networks and explains what happens inside.The second and third courses in this specialization build on this one and explain how to build efficient NN. Even if you're using a software framework NN - it's important to understand whats going on inside when the results aren't as expected.

Cons: The subtitles need proofreading.

Great course. Clear explanation of the materials. I can see a lot of tidy-ing up from the Machine Learning course, e.g. making the matrix dimension consistent, clarifying the notation and meanings. The programming assignment feels more easy than previous one, since we had to read and understand the mathematical formula and then implement those in the code back then.

I'm very glad to the course. I think it is really useful for those who want to take a first try with neural networks and deep learning, and also for those who already have some experience, as it helps to clarify some complex aspects with simple explanations. I have to say that mathematic expressions on the board may seem a little daunting for the non-maths public.

DL seems approachable when Andrew explains it. Lots of great information without getting too bogged down in deep math. I like that the homework did not use one of the popular frameworks and used a lower level approach (leave learning frameworks for other classes).

Several minor errors in videos is annoying, but they are all documented so you can watch out for them.

This is, undoubtedly, one of the best courses on Deep Learning.The material is taught by Andrew Ng in a quite lucid manner as he builds concepts in an intuitive manner.I would love if the course delved a bit deeper on the derivations.Other than that, a perfect course for a beginner like me who knew nothing about neural networks and other Deep Learning jargons yet !

A terrific course even for those with prior deep learning experience. I've used neural networks for many projects over the past few months, but implementing everything from scratch really helped me understand every detail of what is going on "under the hood" of the libraries I've used such as TensorFlow and Keras. I feel much more confident in my abilities now. A+.

Very informational and great programming assignments that made me understand the concept in depth. These hands-on-project are very awesome, makes it easy to learn and want to research more and try different things in the notebooks. It is an awesome course!

I am a beginner and started direct here but having skills for python will go a long way for anyone out there.

One of the best courses I found to learn neural networks and deep learning as a beginner. All the subject matter are well structured and the flow of the module is very easy to follow and understand. Together with the programming assignments, I was able to quickly grab the essentials in deep learning. I highly recommend this course for any deep learning enthusiast.

This is a very good course which does not involve rigorous mathematics and mathematics needed for the understanding and implementing neural networks is explained in an easy to grasp manner. I would recommend this course to anyone who aspires to learning neural networks. I appreciate the team for putting in a lot of effort to give us a high quality course material.

Very insightful, well organized and well taught course. The core concepts of Deep Neural Networks development (which generally appear to be complex and intimidating) are explained methodically in such a nice way that the learners can easily delve into its depths and build their own models from scratch. Much needed knowledge, thanks to deeplearning.ai and Coursera!

Andrew made it look less scary than the name "deep learning" sounds. The only struggle I faced was understanding the math behind backpropagation but eventually by completing the programming assignments which are broken down into pieces and executing each one of them helped in making sense of behind the scenes of Neural Networks. Overall this course is recommended.

One of the best course to get the proper understanding of the concept of neural networks and deep neural networks .The assignments after every week helps a lot to understand the practical application and connect it with the theory. It also increases the fluency on python and hence i recommend everyone to complete this course who wants to explore the deep learning.

Thorough mathematical explanation of neural networks, especially a nice follow-up of the Matlab/Octave based Machine Learning course (2011) of Andrew Ng.

The Python based implementation is a well guided exercise, and calculus self-reproduce-ability is not required. Therefore this course will enlighten you into the maths and a do-it-yourself implemenation in Python.

A very good introduction to Deep Learning. The coding examples are explained well and help a newcomer build functioning neural nets. The math could be difficult for those unfamiliar with matrix multiplication, but the other math (especially the calculus) is handled well and smoothed over by Andrew so the student can get to what is actually the meat of the course.

Having completed the Andrew Ng Machine Learning course, this Neural Networks and Deep Learning course provided the next level of detail and a great introduction to Neural Networks and Deep Learning. The exercises in Python was more aligned to real-world examples however the familar style of Andrews teaching shields you from a need to deeply understand the maths.