Neural Networks for Machine Learning

This is one of those chance in a lifetime courses you have to get to learn from the greats. Geoffrey Hinton was one of the most important and influential researchers to work on artificial intelligence and neural nets back in the 80's. Currently he is working with Google in their AI/deep learning initiatives.
The lectures are complex but Geoffrey does a really good job of providing detailed explanations. The course is mostly theory based and heavy on calculus and linear algebra with only a few programming exercises. I would categorize this within the same space as Yaser Abu-Mostafa's Learning From Data.
There were 15 total quizzes of which you only had 2 attempts. Since i was had very little experience with Calculus and was still cutting my teeth on Python i had to let this one go. I plan on taking this again at some point in the future, but it looks the course has been archived.

I honestly can't understand the multiple 5 star reviews presented on this site about the course. I'm giving it a 1 star which is a bit harsh I know but I'm doing it to offset the number of 5 star reviews here. Honestly I think the course deserves something between 2 and 3 stars depending on your approach to it.

Yes Prof. Hinton is a leading expert in the field but the course materials and the way they are presented are pretty bad! I started the course already with a decent foundation on machine learning wanting to learn more about the specifics of neural nets but it ended up in confusing me more on core concepts. For instance I was reasonably sure about how backpropagation worked until I got to the part in this course about it, which managed to confuse me so much that I had to go look up multiple sources to relearn it again. This happened with a number of different concepts over the course and it's not that the course is "challenging" as some people here say, it's really a question of poor explanations and poor organization of materials (the quizzes are challenging in the sense that they demand a much better grasp on the material than what the lectures give you but this is one of the few positive points in my view).

And sure you can always research things by yourself, and this is a good practice regardless, but in this case it's an absolute necessity because of the poor lectures. In a sense maybe it is it's redeeming feature. It can piss you off so much to be confused in the lectures that you can go and learn the material on your own just out of spite! It can work, but a truly good course that would deserve 5 stars shouldn't have to rely on this!

I actually dropped from the course for a few months after a terrible week 4 and recently out of sheer stubbornness I am trying to complete it, but it feels like a terrible grind. Basically if you already know enough to understand this course well you probably didn't need it anyway but if you don't I would stay away if you have no other alternative as with the wrong approach it seems to confuse more than it helps.

Prof. Hinton is one of the leading lights of Neural Networks, an area of ML research that had been relegated to the sidelines in the 80s and 90s but is now in the limelight thanks to recent advances in the field.

The basic ML course does dip its toes in the neural networks pool, but this course (but naturally) goes much deeper. The material as well as the instructor are excellent, and the course lectures are punctuated by Prof. Hinton’s deadpan humour. This course requires a little mathematical background (such as a calculating derivatives for backpropagation) but the math is explained quite nicely.
My only suggestion in this course would be to have more programming assignments (there were only 4 in the first edition), however, the quizzes do require some programming to answer questions.

Yes there're negative reviews saying this course is poorly designed, too hard to follow, confusing, but after finishing it and earn the certificate I think I make the right call.

Actually, I agree with some of the reviews above. This course is not as polished as other pillar courses on Cousera like Andrew Ng's machine learning. Yet, this the best course I can find to teach me advanced neural network topics in a mooc style, I mean with lectures, quizzes and assignments. And I this style matters to me because I find it hard to learn things in depth just by reading text books (though often enjoyable).

Not to mention that professor Hinton is an authoritative in this field and would cover a lot of background, with anecdote sometimes and help you understand how the machine learning evolves into what it is today (to be exact it's 2012).

I enrolled this course right after I finished Andrew's machine learning and I found a hard time adjusting my learning attitude. Now I feel that if you're really serious about machine learning and neural network, and like me you know nothing about it before, then it's a must you take this course. Why? Just think about all the researchers working on this area and you could get an insight into their best work in such a short time. If that's not what you want, instead you just want a brief overview of deep learning without getting into calculus and probabilities, then choose something else.

In short, whether you should take it depends what you want from the course and what it provides. For me, I want to be serious about it and so I feel the frustration and hiccups I experienced during this course compared what i have learning is nothing to what a real researcher might have experienced. I better finish this review soon and go through this course a second time 'coz I feel uneasy that I only understand 50% but get 100% grade.