21–30 of 46 posts
Personally, I've found that I don't retain much of this sort of material without working through exercises. If you learn the same way, you might want to check out the series of progressive exercises from Andrew Ng here: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial For reference, I have a copy of my solutions here: https://github.com/danluu/UFLDL-tutorial . Debugging broken learning algorithms can be tediou…
What does it take to be good at machine learning such as this? In terms of mathematics, and computer science knowledge? I know how to code through self learning, and I've pretty much solely done web development. So I barely know much CS. Also not very good at math. So what are the essential prerequisites you would say are necessary for doing neat, useful stuff with machine learning?
Linear algebra. Bayesian statistics. MUST know these inside out, upside down.
Vector calculus. Convex optimization.
A boatload of machine learning literature. The ideas coalescing into deep learning are based more than a decade of research.
If you know nothing about math... I can't imagine getting to the point of understanding deep learning (which is a fairly rapidly evolving area) without at least 2-3 years of very hard work.
This class is a reasonable attempt to give a quick intro to one major source for DBNs https://www.coursera.org/course/neuralnets understanding this course is a good benchmark.
This is a cool tutorial! It's ironic that deep neural networks have become the biggest machine learning breakthrough of 2013: they were also the biggest machine learning breakthrough of 1957. The idea dates back to the Perceptron, one of the oldest ideas in AI. One thing to note: although there was a lot of initial excitement about Restricted Boltzman Machines, Auto-encoders, and other unsupervised approaches, the be…
The next big boom in AI (ignoring some logic/rules-based research in the 70s that I don't think is very interesting from an AI perspective) occurred in the 80s, when computational power increased and researchers discovered/rediscovered neural approaches, including the obscure 1974 research on backpropagation. This led to tons of press and funding from governments who dreamed of killer AI robots and what-not. But, once again, imagination raced ahead of reality and funding dried up when said robots didn't materialize. The field didn't really die off, but funding in AI went way down, leading to another major "AI winter".
I'd say the next big era of AI is the one we're in, driven largely by applied statistics that became known as "machine learning". This has been by far the most successful era, and has probably added 100s of billions of dollars to the economy (I'd argue Google is a machine learning company, for example). I think it's also the most pragmatic era, as people in the field have really learned from the past mistakes of overpromising. In fact, when I was studying "AI" in grad school, my professors warned me to always refer to what I did as machine learning because the concept of "intelligence" was such a joke to so many in the field.
theano: http://deeplearning.net/tutorial/
pylearn2: http://deeplearning.net/software/pylearn2/
NIPS, a big ML conference, is in December, so expect to see a large amount of new ideas and applications re: deep learning to come out of that.
This is a really good write up. For people looking for practical experience with these types of methods, I'd also recommend checking out theano and/or pylearn2 (which is built w/ theano). theano: http://deeplearning.net/tutorial/ pylearn2: http://deeplearning.net/software/pylearn2/ NIPS, a big ML conference, is in December, so expect to see a large amount of new ideas and applications re: deep learning to come out of…
Personally, I've found that I don't retain much of this sort of material without working through exercises. If you learn the same way, you might want to check out the series of progressive exercises from Andrew Ng here: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial For reference, I have a copy of my solutions here: https://github.com/danluu/UFLDL-tutorial . Debugging broken learning algorithms can be tediou…
Earlier quoted context omitted.
Yep the ideas from the 50's have definitely reappeared now we have the compute power and methods to implement them at a large scale. That article gives a nice perspective. One of the best breakthroughs has been this notion of layer-wise pretraining, which allows the backpropagation algorithm to not get stuck in local minima so easily. It provides a good guess to the starting starting points for the weights. Otherwise…
That's what I thought too! But according to my friends on the Google Brain team, unsupervised pretraining is now thought to be an irrelevant detour. In 2006, Hinton introduced greedy layer-wise pretraining, which was intended to solve the problem of backpropagation getting stuck in poor local optima. The theory was that you'd pretrain to find a good initial set of connection weights, then apply backprop to "fine-tune…
Machine learning is really just a form of non-human scripting. After all, every ML system running on a PC is either Turing equivalent or less. An analogy would be something that tries to generate the minimal set of regular expressions (that match non deterministically) which cover given examples. The advantage of an ML model vs a collection of regexes is many interesting problems are vulnerable to calculus (optimize) or counting (probability, integration etc.)
So like good notation, the stacking allows more complicated things to be said more compactly. But more complicated things need more explanation and more thinking to understand.
An excellent 101 article.
This is a cool tutorial! It's ironic that deep neural networks have become the biggest machine learning breakthrough of 2013: they were also the biggest machine learning breakthrough of 1957. The idea dates back to the Perceptron, one of the oldest ideas in AI. One thing to note: although there was a lot of initial excitement about Restricted Boltzman Machines, Auto-encoders, and other unsupervised approaches, the be…
The history of AI is really interesting. Perceptrons were extremely oversold by their inventor, Frank Rosenblatt after he introduced then in 1958. This led to a lot of funding and interest in AI and perceptrons. Then, in 1969, Marvin Minsky coauthored a book Perceptrons which harshly criticized how underpowered perceptrons were. Most famously, the book proved that a perceptron could not model a simple XOR function. I…
1. Linear Regression (which, admittedly, was amazing)
2. Fourier Analysis (which is linear regression on orthonormal bases of functions. it blew people's minds)
3. Perceptrons (which is linear regression but with a logistic loss. it went back to its old name of "logistic regression" once its insane cachet of biological plausibility faded)
4. Neural Networks (stack of logistic regressors. popular with people who didn't know how to filter their inputs through fixed or random nonlinearities before applying linear regression)
5. Self Organizing Maps and Recurrent Nets (which were neural nets that feed back on themselves)
6. Fractals (which is recursion. they were useful for enticing children into math classes)
7. Chaos (which is recursion that's hard to model. useful for movie plots)
8. Wavelets (which is recursive Fourier analysis, and probably still way under-used)
9. Support Vector Machines (which replaces logistic regression's smooth loss with a kink that makes it hard to use a fast optimizer. often conflated with the "kernel trick", which appealed to people who didn't want to pass their inputs through nonlinearities explicitly)
9. Deep Nets (which are bigger neural networks. the jury's out whether they work better because they're deeper, or because they're bigger and require a lot of data to train, or because they require a programmer to spend years developing a learning algorithm for each new dataset. also whether they do actually work better).
Once this Deep Net thing blows over again, my money's on Kernelized Recurrent Deep Self Organizing Maps.
(On a serious note: MNIST is considered a trivial dataset and doesn't require the heavy machinery of deep nets. linear regression on almost any random nonlinearity applied to the data (say f(x;w,t)=cos(w'x+t) with w~N(0,I) and t~U[0,pi2/]) will get you >98% accuracy on MNIST.)