This is a great course on deep learning! Andrew explained things in a very clear way using plain language. I love the coding exercises that were carefully designed in Jupyter Notebook by the instructor and all the TAs. Thank you very much! I can tell you all must have put a lot of time and thoughts into the coding exercise. All the instructions in the notebook made the homework easier to handle.

I think I'll repeat the coding of the NN at the end from scratch as it hand-holds you a bit, so the odds of making errors and thus learning from them are not so high.

Apart from that gives you all the concepts you need to know to build fully connected NN (if you like theory you have to know or learn the calculus / algebra parts for the derivatives derivations if you want to do them by yourself).

Loved the course - had to slow myself down to really absorb and toy with the material. Having taken the earlier course in octave, a little adjustment was necessary, but now feel ready to move through the remaining modules and eventually start playing with Tensorflow.

Can't imagine anyone claiming to be proficient in ML without the level of hands on, bottom-up approach that this course provides.

This course is well organized and the assignment is well proposed. By studying this course the learner can get a concrete understanding of neural network and get practice by programming using Python. I have learned a lot from this course, even I already have the basic concept on machine learning. I would recommend this course to beginner or student who have already take machine learning course.

I have a sound foundation of Deep Learning after I studied this course, the lectures repeat the core concept of forward/backward propagation again and again from single sigmoid node neural network, single hidden layer neural network, to multi-layers neural networks. After repeatedly think about each W, b, Z, A, dW's shape, I now feel super clear about the NN and how to calculate it. Thank you.

A very nice course, it is well written and well taught. The exercises are interesting, albeit a bit repetitive, and the concepts are presented and explained in a timely manner. The course pace is good, the notation and explanations are clear, and the instructor and mentors provide a lot of help. It is a great introduction to the field, either if you have previous programming experience or not.

Everything about this course is perfect. From wonderful lectures to beautifully commented ipynb that helps a lot while solving programming exercises. Discussion forum helps a lot. "Heroes of dee learning" series is quite inspirational. I would suggest to follow this course with deep learnig book by Goodfellow, Bengio and Courville. You can find the e-book here: http://www.deeplearningbook.org/

A very intuative and interactive course to learn from very basic to a complete Neural Network with forward propagation and backward propagation. As the course progress one can learn how to built deep Netwrok. Explanation from Andrew is awesome specailly those critical calculus parts which are fundamental for backward propagation. Overall such an awesome course for anyone from any background.

Excelente curso, felicitaciones en ofrecer tan buen material a disposición del mundo entero. El curso está muy bien estructurado, la profundidad es adecuada y las estrategias instruccionales son efectivas, realmente consigue el objetivo de inspirar y mostrar herramientas para desarrollar redes neuronales. Recomendación: Ofrecer un material básico de python antes del curso o hacerlo en octave.

This is a really great course for those who have no prior background in machine learning. I had only known about the perceptron and had some mathematical background on linear classifiers. However, I think anyone could benefit greatly from this course. It allows you to learn and understand the fundamentals of building a deep neural network from scratch, instead of just using it as a black box.

Excellent introduction to Neural Networks. Andrew does a pretty good job of explaining the math behind forward-propagation and back-propagation.

There is a confusion sometimes between lowercase and uppercase matrices and the dimensions they are supposed to have, but the quizzes and programming assignments force you to figure it out.

Good job!

I know a lot of work goes into making these classes.

Great course !

+ : Structured exercises, very well paced / articulated set of lectures, Professor Andrew Ng, Format of the learning in general

Improvements : Derivations would be nice (for back propagation), Applications to problems other than a cat image classifier, Showing the 'bigger approach' to deep learning (how to proccess your data, what questions you should be asking yourself, etc..)

If you follow the instruction carefully, you can finish the course even if you are not familiar with neither English nor Python. As a graduate student in a totally different science field, I'm now learning the basic concepts of machine learning and deep learning through the series of Andrew's classes. Thank you very much for the endeavors of whom all of the people contributing to this class!

The way every single detail is explained in this course has no equivalent in any other courses I took so far. It helped me a lot with my PhD current project to build my own neural network model using Julia Programming language, even though the course is explained in Python (which I have excellent skills in Python) but it is what I learned here can be easily implemented in any other languages

This course is truly amazing. The explanation of Dr. Andrew is very good to understand the concept of machine learning. I was able to acquire important concepts easily and intuitively. Also, the homework of programming is so interesting. I was able to review what I learned through lectures by the programming assignment. This is a one of the best machine learning courses I have ever taken !

Sir, Andrew explained every concept so well and step by step through pictorial representation that I was able to visualize each and every gradient equations in my mind, which helped a lot to understand the concepts.

I was not able to understand the back propogation concept in machine learning course but here i got that concepts very well. It gave me great confidence. Thank you very much Sir.

I have learned a lot of deep lerning technisisms and stuff that i have heard about but never really understood... Andrew is a great teacher and the methodology of short concise videos help my development a lot. Also the programming assignments help you improve your understanding of the content, by making the practice part of the theorical part learned in the videos. Absolutely recommendable

One of the best introductions to DL,

Implementation of each function from scratch, by using helper functions and division of bigger problem into smaller problems and integrating it gave a good approach for general problem solving as well.

Appreciate this Course exists, and Thank You Andrew NG for doing something great for human kind, many people are going to use this to do things unimaginable

This is by far the best course on neural network practitioners. Most of the courses either tend to suddenly go too deep into the concepts with heavy lifting required in going through multiple papers or tend to just focus on tools like tensorflow. This course has the balanced approach. The only think I wish more is to point to list of reading resources for beyond the course optional reading.

This course is very helpful if you have some idea from the foundation course Machine learning. Otherwise also it is easy to understand. I got more intuition after doing the first 5 weeks of machine learning course. The intuition which I got pushed me to do weekly homework and watch videos. I am so glad that I know the basics well now. Moving to pursue further courses in this specialisation.

Prof Ng is a great teacher and is good at making the difficult material very easy to learn. I am very interested in the DL. Before I took this class, I found that since this field is very new so all the material you can find is a little piece and not systematical. This specialization is a wonderful and systematical, easy to learn and fun. Thanks for the great work those teacher have done .

This is an amazing course for getting started with deep learning . I have applied deep learning on few projects I have done before using tensorflow and pytorch but then I never understood much about the working of these neural networks. This course will let you know all the intricate details regarding how the neural network works , it's so simple and beautifully explained by Sir Andrew N.G

This was my second-course online course and I was going with the normal "keep it casual" behavior till I bumped into a roadblock !! my assignment 's answer was not correct. Since then I gave extra attention and found every video so much educational, Andrew is a seamless teacher who will remind you to stay alert :) the community is very helpful, friendly. Looking forward to the next courses

This one course on its own has taken me from absolute beginner to really knowledgeable and keen on making my own deep neural networks, I can now implement models and know how to completely implement my very own complete models, choosing my hyper-parameters, etc.. Thank you for this great opportunity, Coursera! And huge thanks to professor Andrew Ng. for his dedicated effort on this course.

Very great course!

If you have finished Andrew Ng's Machine Learning course, you may find this a little bit boring and you may finish it only in 1-2 weeks. But still, the programming assignment is worth it. You will build a dynamic neural network, meaning you don't have to change the inner working of the neural network if you want to change the number of layers or number of units per layer.