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University of Toronto

Neural Networks for Machine Learning

University of Toronto via Coursera

This course may be unavailable.


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


3.7 rating, based on 26 Class Central reviews

Start your review of 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…
  • 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 s…
  • Kiran Karkera
    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 to…
  • Sha Liu
    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…
  • Bill Griffith
    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.
  • Profile image for Aditya Khokhar
    Aditya Khokhar
    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.
  • Profile image for Cameron McPherson
    Cameron McPherson
    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.
  • Anonymous
    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.
  • Dolly Ye
    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.
  • Jakub
    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.
  • Profile image for Pablo Torre
    Pablo Torre
    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.
  • Anonymous
    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.
  • Anonymous
    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.
  • 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.
  • Anonymous
    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.
  • Nihal Balani
  • Josh
  • Stephane Mysona

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