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Neural Networks for Machine Learning

Found in Deep Learning, Artificial Intelligence, Neural Networks

Provider Coursera
Cost Free Online Course (Audit)
Session Finished
Language English
Certificate Paid Certificate Available
Effort 7-9 hours a week
Start Date
Duration 16 weeks long
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Overview

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Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform.

Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).

NOTE: This course will be ending soon and the last day for enrollment will be October 10, 2018.

Taught by

Geoffrey Hinton

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Reviews for Coursera's Neural Networks for Machine Learning
3.7 Based on 26 reviews

5 stars 50%
4 stars 19%
3 star 4%
2 stars 8%
1 stars 19%

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Pedro Q

1.0 2 years ago

by Pedro is taking this course right now and found the course difficulty to be medium.

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.

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Bobby B

5.0 5 years ago

by Bobby completed this course, spending 6 hours a week on it and found the course difficulty to be very hard.

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.

20 people found
this review helpful

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Kiran K

5.0 5 years ago

Kiran completed this course, spending 10 hours a week on it and found the course difficulty to be hard.

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.

Full Review

7 people found
this review helpful

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Sha L

5.0 3 years ago

Sha completed this course, spending 5 hours a week on it and found the course difficulty to be very hard.

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…

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Bill G

1.0 2 years ago

by Bill completed this course.

Prof. Hinton may be one of the leading lights of Neural Networks, but the style of this course will prove to discourage any novice who hopes to gain insight into theory behind neural networks. According to Prof. Hinton himself, he put this course together in his spare time, and I believe that the poor quality of the material reflects his effort. I agree with the other reviewer, it's a horrible course. Very few examples, no diagrams, no mathematical detail. Prepare to do a lot of discovery on your own from other sources in order to pass this course.

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Aditya K

1.0 a year ago

Aditya is taking this course right now.

Well, I rarely write reviews (especially about online courses) but this course was so so horible I had to write the review. The prof may be an expert in the field but that doesn’t make him a good teacher. The course in totality is absolute non-sense. It fails on several levels lik it has almost zero coding involved and very negligible Maths. I mean if u look at it the whole course material are nothing but text based PPTs and not even a single concept has been explained well enough. For instance the family tree example was such a time waste. I can go on but enough to say that in case I was able to give a negative rating about this course I would have happily done that.

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Anonymous

1.0 2 years ago

Anonymous is taking this course right now.

Just horrible. The quality of the material course is very poor. The covered field is very interesting but the course is not intelligible. The course of Andrew Ng was very clear but this one, you must take course about neural network to be able to understand. Very few schema, no example, no diagram. You have to be very very motivated.

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Dolly Y

5.0 3 years ago

by Dolly is taking this course right now, spending 8 hours a week on it and found the course difficulty to be hard.

Geoff Hinton is one of the founding fathers of neural network when everyone jumped ships in the 90s.This course takes a more theoretical and math-heavy approach than Andrew Ng's Coursera course.If you are interested in the mechanisms of neural network and computer science theories in general,you should take this! An intellectually invigorating experience.

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Jakub J

4.0 2 years ago

Jakub completed this course, spending 6 hours a week on it and found the course difficulty to be very hard.

This is one of the hardest MOOC I ever took, even though prof. Hinton is a great researcher, some topics lack explanation so additional resources are required (sometimes they are provided). Biggest problems are technical ones like error in quiz and unavailable lecture note mentioned in the video.

Overall this is still very valuable course with lots of knowledge.

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Pablo T

5.0 4 years ago

by Pablo completed this course, spending 10 hours a week on it and found the course difficulty to be very hard.

This is a hard class, it goes deep into the math behind neural networks and it goes deep into the design implementation of such network. It is also a unique opportunity to learn from someone who is a pioneer and one of the lead thinkers in the field of neural nets.

2 people found
this review helpful

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Anonymous

5.0 7 years ago

Anonymous completed this course.

If you interested in neural networks, this course may discover you a whole new world of possibilities. It is not easy, but is is definitely worth the effort.

2 people found
this review helpful

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Cameron M

1.0 a year ago

Cameron is taking this course right now.

Geoffrey has a lovely accent and is a pleasure to listen to.

That marks the end of my compliments for this class.

Having completed and thoroughly enjoyed Andrew Ng's Machine Learning, I figured this was next step to enhance my understanding of neural networks.

This course is aimed at those that have probably taken 2-3 years of math / Masters in Machine Learning.

I found his language unnecessarily obtuse, that the end-of-week quizzes sported questions which were entirely incongruent with the weekly teaching, and that the flow of concepts was dis-jointed.

This marked a highly dissatisfying experience, and deserved to be dropped.

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Anonymous

2.0 a year ago

Anonymous is taking this course right now.

jumps between overly detailed explanations of specific analogies then drops random advanced math with undeclared meanings as quiz without giving any reason or supporting explanations, expects you to already know a lot of advanced math. Only take this course if you are prepared to open 25 Chrome Tabs to find out what the heck he is talking about.

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Jae-seung L

5.0 2 years ago

by Jae-seung completed this course, spending 3 hours a week on it and found the course difficulty to be medium.

I would recommend this course to anyone who is serious about learning neural networks. I've got a better understanding of the concept, history, and techniques, which may not be available in other online courses or may not be easily grasped by reading papers only.

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Anonymous

5.0 9 months ago

Anonymous completed this course.

Great course. Great professor and material. If you are serious about machine learning this is the course. A little more math than other courses, but nothing too difficult, everything can be found in the class handouts.

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Lanting G

5.0 3 years ago

by Lanting completed this course.

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Colin K

4.0 4 years ago

by Colin completed this course.

0 person found
this review helpful

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Stephane M

4.0 3 years ago

Stephane completed this course.

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Hari R

4.0 3 years ago

by Hari completed this course.

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Nihal B

5.0 4 years ago

Nihal completed this course.

0 person found
this review helpful

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Neural Networks for Machine Learning

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Reviews for Coursera's Neural Networks for Machine Learning
3.7 Based on 26 reviews

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