Andrew you did it again! This is the best intro theory and implementation course on Neural Networks out there. It combines enough theory (optional Calculus/Linear Algebra) and full implementation. The discussion groups are great for hints when you get stuck. Thank you to all the assistants and TA's who put in so much time to this course!

Now, for anyone who is debating taking this, having a calculus and linear algebra background will definitely help you in this course for the theory, but it's not a necessity at all. Some prior Python experience is needed as you will need to understand how functions are being called, but that wouldn't take a lot of time to get caught up on, but would require additional effort.

Fantastic course! As someone who has done several online tutorials that use frameworks like TensorFlow or Keras, even having implemented things like deep reinforcement learning agents and image classifiers with these frameworks, I've never really gone through any formal training on much of the lower-level concepts/mathematics of deep networks. But this course cemented a lot of the fundamentals about deep learning into my brain that I was missing before, organizing topics in a clear and concise sequence of videos/lessons that really helped me keep things organized in my own head. Doing the exercises in a "manual", framework-less way using just standard Numpy was an awesome and enlightening experience too!

One of the best Deep Learning courses (probably 'THE' best) around. Been a fan of Andrew Ng's sessions after I took his Machine Learning course in Coursera.

The course is perfectly designed such that its complexity increases gradually every week. The instructor makes sure that the participants follow completely.

Some points about the course -

1) Assignments are made too easy (too much hand-holding). But maybe the others who are new to programming might appreciate it better.

2) Andrew laboriously reiterated the points that required understanding.

Sometimes, I felt the course topic to be highly repeatative but later after completing the assignments, I felt it was required to be so and it was totally worth it!

First of all, thank you so much for this course I find this course very useful, I get to know more things. I would definitely recommend this course to my friends and colleagues but only if they are interested and also love to do math stuff because a non-math lover can't get much from this course, obviously the mathematics section of this course was tough to understand and had a bad time in deriving those algorithms and programming. I would also like to state that the quiz and examination section of this course is harder no hardest and that's completely opposite in terms of difficulty like you'll figure the teaching part was easier than understanding and the understanding is easier than programming stuff.

As a beginner who is interested in Deep Learning, this course was very useful an informative. The explanations given by Prof. Andrew were to the point and precise. However, detailed explanations behind the mathematics(calculus) could've been given as optional videos. The quizzes and the programming exercises were also very challenging at a beginner level. They quizzed us not only on the equations but also on the concepts. The only drawback in this course that I faced was trying to submit my programming assignment through Jupyter notebooks. They can very annoying at times. Also, the discussion forms were very active with the mentors and fellow student who would quickly help by replying to your questions.

This course is a very thorough introductory review of neural networks that doesn't require expert level knowledge in some of the underlying math like calculus, but nevertheless manages not to talk down to you. In fact, the straightforward way the material is presented inspired me to learn calculus on my own to back up the material. Regardless, it gives you (almost) everything you need to start coding neural networks on your own. Where I did have some trouble it was owing more to lack of experience with Python and the Jupyter environment. I also would have liked a little more visibility into the data we were inputting, although I think that may be covered more in classes further on in the specialization.

Hi to all team who had put their Mount Everest's height like efforts for making this a fab course and self explained assignments.And special thanks and love for my dear Andrew sir for teaching too smoothly and always relaxing their students by saying "If you don't get it,don't worry, we will see it after sometime".One more Special credit and mention for team who built these amazing self explained and too easy to understand python notebooks so that even a kindergarten student can get this and one only needs to pay attention on making skills and not on other things like how to fetch data,and how many and which libraries to be added etc.

Thanks and kudos to all team and loads of respect to DEAR ANDREW SIR.

I found this course absolutely excellent. The structure and approach are absolutely great, and I am very happy that you force students to understand the mathematical underpinnings of backpropagation instead of letting a DL framework do the heavy lifting for you. Engineers need to understand what they do deep down.

My only improvement suggestion would be to provide a more detailed explanation of why we do the matrix multiplication & transpose in the computation of dW and of dA[l-1]. It turns out that in the case of dA[l-1] the explanation goes to the heart of reverse-mode differentiation and how to avoid combinatorial explosion. Cfr's Colah's blog excellent paper on backpropagation for details.

I liked this course very much. I have done coding and trained models in Pytorch and didn't have strong grasp in the math's part i.e Gradient and derivates that is the why i have taken this course at the first place. Though the course doesn't contain everything but it has given me enough knowlegde to start with deep-learning.The quiz and programmning excersice are really good. I have to think enough at some part and have done mistakes many times but got my concepts cleared. And thanks to coursera team for approving my financical aid.And though review may be good or bad depending on the person but i have learnt what i want to learn and it is good enough rahter than youtube or online material.

I have done a bit of neural networks and machine learning already in uni, so this was a bit of revision for me. However, I still learned quite a few things. Andrew just explains it in a way that is very oriented to actually using the information in a project, or otherwise in a way that makes the info feel natural to digest. Things I remember struggling with in my uni course felt a lot easier after the course and it only took me 5 days to complete. That's not even a week of your time.

Long story short: even if you are not a complete beginner to the topic, I still recommend to take the course, as it is a quick refresher of all the relevant topics and takes relatively little time to complete.

Absolutely Fantastic. I thought the programming assignments were a little too easy, but that's probably because I am familiar with python programming. I must say that the structure of the code really helped me focus on the core algorithms and vectorization (using numpy methods), so, in retrospect, it is probably a good way to make the student focus on the core concepts. I wish, however, there were some (more) optional lectures on the math and some more detailed derivations and some "optional" practice problems on doing partial derivatives etc., just to cement some important concepts such as back propagation. Highly recommend this to students wanting to learn the basics of neural networks.

I gained so much valuable knowledge from this course through the weekly Assignments. Learning by doing is real. The lecturer prepared exciallent materials especially the programming assignments that give us clear guidelines step-by-step, making it easier to understand. He took the effort so much to prepare the course. Additionally, he explains all the complicated points clearly including the derivation of various equations that require calculus knowledge which I did not expect to see him explain. Hopefully, I will be able to find time to take the rest course in this series to find some ideas of neural networks and deep learning that can be implemented in my master's and Ph.D. research.

Awesome course. I have fairly little previous math experience though I have been working on some calculus and LA immediately before/while taking this, and all the topics were easy enough to understand how they are supposed to work. Much recommend.

One small thing I think could have helped a bit is the practical examples do a little bit too much hand holding. It makes it a little hard to know if you are actually grasping the knowledge or just able to tell what to do based on what information has already been made available from the templates and such. Had to step outside of this and try to do some of it on my own to see which pieces weren't fully making sense. But still, awesome course!

Great, just like the first Intro to Machine Learning course Professor Ng distributed. Same style with improvements made in course design. For example, notation is much more consistent this time around probably because it is a more focused course unlike the first one. I would say taking Intro to ML first would help as it is a perfect primer for this course. Also, I'm glad they've decided to use Python which is just much more general purpose than MatLab. I would also say a solid grasp of the language is needed as a lot of boiler-plate code is provided and understanding it could be difficult if not otherwise comfortable with Python. Looking forward to continuing the full specialization.

Amazing course! The creators are very good teachers. Materials have the right mixture of motivation, real world examples, theory and practice. I also like Andrews presentation style - one can really feel that he truly cares about the students being given good information and getting encouraged to learn. The assignments were also very well made - everything works, the code is good and there is so much help in the context/comments (eg. someone could even finish the labs without understanding the subject) - but this basically allows each student to choose how much effort he/she wants to put into the subject (also meaning how much knowledge she/he wants to absorb during this course...)

I found this course very helpful in furthering my understanding and clearing a few doubts that I had from the Machine Learning course. I seem to understand back propagation much better now.

This course also helped me give a structure to the steps involved in actually building a Neural Network... gives me more confidence.

My only issue was with the programming exercises. I felt they were very tightly structured, maybe because of the automated grading system. It was almost impossible to go wrong. More flexible and open exercises, I think, will help in learning the real intricacies of building a NN from scratch. Don't really know enough to comment on how this change can be incorporated

The course is well structured and the programming exercises are so detailed, I am going to refer to them in future while implementing neural networks. The best part about the course is, Andrew Ng actually taught the math behind the network. Rather than taking his students through a library function for neural networks in python, he taught his students how to code from scratch while also covering nuances such as suitable activation functions for different cases and ideal values for weights. The documentation for programming exercises is very detailed and must have taken plenty of time for those who worked on it. Recommend it for anyone wanting to start. Kudos to the instructors!

The programming assignments (things which the student had to fill in) seemed repetitive and very limited (e.g. mostly needed only mathematical formulas to be filled in using numpy). However, to keep the grading similar and also cater for less advanced users simultaneously, the assignments could be tiered by beginner-intermediate-advanced (by concealing more and more stuff) but still grade based on the current beginner level only. So, one could start with advanced and then reveal more to get to intermediate in case he is not able to solve etc .. May be optional bonus grades for solving it at advanced level etc.

Otherwise a good course overall to get intuition into deep learning.

I took the Machine Learning course last year, and my only complaint was that the neural networks part was a bit confusing. (Once the bias unit had to be in the weights matrix, once it had to be removed in order to perform some calculations.) The formulas also didn't work all the time, and you needed to figure out the proper shapes (using transpose operations) on your own.

In this course, the notation and explanation is much clearer. Keeping the bias unit out of the weights matrix in the first place makes everything easier. (Of course, the video quality is also better.)

Thanks Andrew; you really listened to your audience and figured out how to explain those concepts more clearly!

A very good entry-level course. Made me recall linear algebra and calculus but in a good sense :) Andrew does a great job in explaining the material and his "if you don't get it exactly - don't worry - there are plenty of deep learning practitioners who don't get it either and are still very successful" - is very reassuring. He is the type of the lecturer that explains to you the material in such a way that those who do not have enough background feel very comfortable as well as those who have enough background do not dose off either.

In general the course made me want to go on with the next courses in order to get more insights regarding what's going on inside deep learning.

After the "Machine Learning" course from Professor Ng I saw this specialization and gave it a try. He manages to keep a great quality of content through out the entire course and explains everything in a great understandable way. The interviews he added to this course were especially great to listen to and hear how some of the pioneers of artificial intelligence see their own field and hearing about their stories/origins motivates one to really dive into the deeper topics and also develop interest in a specific field of AI. I really recommend this specialization to everyone who wants to dive deeper into AI and even gain new motivation on how to approach such a complex topic.

This is a really useful course to learn the basics of Neural Networks and the intuation behind it!

I have been working with Neural Networks for some years now. Recently I started preparing for job interviews in the field of Machine Learning, so I wanted to refresh my knowledge about the "theory" behind deep learning. Surprisingly, this course made me even learn some concepts and ideas that I had never considered before!

Everything is explained in a very intuitive way. Normally, in this field, we tend to work systematically without even considering what we are doing or why we do it this way, This course helped me easily understand Neural Networks in a way I will not forget.

Oh My God, my first Coursera course that i have finished to the end.. Supper happy and supper excited till I go to the next one. It is so engaging that even working on a temperature above 30 in no AC room did not slow me down. I also started this course while i was officially in vacation since I could not wait till i get back from vacation. Anyways, Very good content, easy to follow and the fact that I had to implement all the theory right away was just super. I learned not only the power of NNs but also my favorite programming language Python. Any one with a motivation and interest in DP should take this course because it gives the foundation in the best way possible.

This tutorial was so elaborated. And in each week Andrew Ng tried to recap important findings from previous lessons which were helpful. Sometimes it looks fuzzy to recognize if the instructor is referring some notation as raw or vector form. But mostly it was nicely designed. I love the way programming exercise was designed. It can provide the basis to build a neural net from scratch. Considering all levels of users, he gently represented all the complex term like derivative in a simple way. Maybe for the future suggestion, Besides handwriting, if those calculations of those function can be displayed in animated design, then it's possible to make it simplified enough.

Andrew is an exceptional teacher. Thanks to him, I clearly understood the structure of a neural network and the functioning of the whole network starting from the single neuron.The mathematics behind a neural network, which until recently seemed very difficult to me, is now very clear.

This course is even better than the one on Machine Learning of Andrew Ng because here you can directly use Python with the Numpy library and all the part of the exercises and practice is, in my opinion, much better structured and clearer than the other course. I recommend it to anyone with an interest in Artificial Intelligence. I can't wait to continue my Deep Learning Specialization.