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That's awesome to hear! I'm taking the Coursera class now, and it's been great so far. It just started a couple weeks ago, so it definitely isn't too late to join! https://www.coursera.org/course/ml
thanks very much for pointing this out, joined the class now, hope i can catch up
OT: His online Machine Learning class last year was great. He is the best professor I've ever had, and explains things so clearly that you understand them the first time. You are lucky if you ever get to work or study under him.
I've said this before, but deep learning is terribly powerful precisely because you don't have to spend lots of time doing feature engineering. Multi layer networks that are trained in semi-supervised, unsupervised and supervised fashions all can now produce networks that meet or beat the state of the art hand created models for speech, handwriting recognition, ocr, and object recognition. We are only just beginning…
There are still applications where e.g. random forests beat the crap out of all kinds of deep learning algorithms in (a) training time (b) predictive quality (c) prediction time.
We should stop hyping this. I am a researcher working in deep learning myself, but the current deep learning hype is actually what makes me worry that I will have trouble getting a job because industry will be disappointed a third time.
There is a very quick reference to the person who inspired him, Jeff Hawkins, whose book is worth a read: http://www.amazon.com/On-Intelligence-Jeff-Hawkins/dp/080507... Edit: update link
This is guaranteed not to be terribly general, considering the many bits of matter on this planet that exhibit intelligence without a neocortex. By many, I mean ones that hugely outnumber humans.
So very interesting stuff, but not the answer that I think he wants it to be.
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thanks very much for pointing this out, joined the class now, hope i can catch up
Haha me too late to the party. I just joined. Frantically watching the video lectures since the assignments are due today (hard deadline)!
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Please think about what a source is. This isn't reddit.
Bullshit. There is nothing wrong with using personal experience for reference so long as it is properly cited. It beats "This guy is wrong (source: random blog post that I didn't really read)", anyway. Edit: To be clear, I don't love the Reddit snowclones, but there's nothing wrong with the sentiment behind "I'm a scholar in this field, and I think this guy is a hack."
I agree with the sentiment, but "properly cited" suggests a bit more than a one line comment from a username with barely a handful of posts (generally pertaining to bitcoin and intermediate level networking certifications) on an anonymous website.
>It beats "This guy is wrong (source: random blog post that I didn't really read)", anyway.
I have to disagree. At least a random blog post presents the potential for useful information or a fully articulated opinion. What we here is 4 words and an unsubstantiated appeal to authority.
There is a very quick reference to the person who inspired him, Jeff Hawkins, whose book is worth a read: http://www.amazon.com/On-Intelligence-Jeff-Hawkins/dp/080507... Edit: update link
https://www.groksolutions.com/htm-overview/education/HTM_Cor...
There are some subtle differences between HTMs and straight up deep learning, mainly the requirement for HTM data to be temporal and spatial.
I know Andrew used to sit on an advisory committee at Numenta, I don't know if he still does.