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I am 15 years into this computers thing and this blog post made me feel like "those guys are doing black magic". Neural networks and deep learning are truly aw

by motoboi 11y ago

I am 15 years into this computers thing and this blog post made me feel like "those guys are doing black magic". Neural networks and deep learning are truly awesome technologies.

deleted 11y ago
[deleted]
dr_zoidberg 11y ago
They are, but once you start learning about them, you realize the "black magic" part comes mostly from their mathematical nature and very little from them being "inteligent computers". A neural net is a graph, in which a subset of nodes are "inputs" (that's where the net gets information), some are outputs, and there are other nodes which are called "hidden neurons". The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net. For example I-H-O is a tipical feed forward net, in which I (inputs) is the input layer, H is the hidden layer and O the output layer. All the hidden neurons connect with all the input neurons "output", and all the output neurons connect to the hidden neurons "output". The connections are called "weights", and the training adjusts the weights of all the neuron with lots of cases until the desired output is achieved. There are also algorithms and criteria to stop before the net "learns too much" and looses the ability to generalize (this is called overfitting). In particular, a net with one hidden layer and one output layer is a universal function estimator -- that is, an estimator that can model any mathematical function of the form f(x1, x2, x3, ..., xn) = y. Deep learning means you're using a feedforward net with lots of hidden layers (I think it's usually between 5 to 15 now), which apply convolution operators (hence the "convolutional" in the name), and lots of neurons (in the order of thousands). All this was nearly impossible until the GPGPUs came along, because of the time it took to train a modest network (minutes to hours for a net with a between 50 to 150 neurons in one hidden layer). This is a very shortened explanation -- if you want to read more I recommend this link[1] which gives some simple Python code to illustrate and implement the innards of a basic neural network and you can learn from the inside. Once you get that you should move to more mature implementations, like Theano or Torch to get the full potential of neutral nets without worrying about implementation. [1] http://iamtrask.github.io/2015/07/12/basic-python-network/ http://iamtrask.github.io/2015/07/12/basic-python-network/
- beambot 11y ago
  You could say very much the same about the brain... > [...] the "black magic" part comes mostly from their mathematical nature and very little from them being "inteligent computers". A brain is a graph, in which a subset of neurons are "inputs", some are outputs, and others are "hidden". The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net. The deep question about deep learning is "Why is it so bloody effective?"
  - dr_zoidberg 11y ago
    I work in the field, and while some models are based on biological structures/systems, there's a lot of fuzz about them being "based on biological foundations" that is now best avoided. Yes, it is true the model is based on them, but it's a model that only covers very little of the real complexity. So in a sense, it's naive to say "put a billion neurons in there and you'll get a rat brain" (as was publicized one time). The effectiveness comes from their non-linear nature and their ability to "learn" (store knowledge in the weights, that is derived from the training process). And black magic, of course!
  - Lawtonfogle 11y ago
    If there is magic to be found, it may be in that question. Why about graphs (namely the subset that are deep neural networks) allow them to not only contain such powerful heuristics, but also allow them to be created from scratch with barely any knowledge of the problem domain. As a side note, I was playing a board game last night (Terra Mystica I believe) and wondering if you could get 5 different neural networks to play the game and then train them against each other (and once they are good enough, against players). I wonder how quickly one could train a network that is unbeatable by humans? Maybe even scale it up to training it to play multiple board games til it is really good at all of them before setting it lose on a brand new one (with a similar genre). Maybe Google could use this to make a Go bot. But what happens if this is used for evil instead? Say a neural network that reads a person's body language and determines how easily they can be intimidated by either a criminal or the government. Or one that is used to hunt down political dissidents. Imagine the first warrant to be signed by a judge for no reason other than a neural network saying the target is probably committing a crime...
dchichkov 11y ago
They are doing matrix multiplications. To pass input a single time through even some very large neural network - it is a relatively fast operation (if compared to training such a network, that is). Training requires data centers and arrays of GPUs. Passing the input through the network - usually you can get away with a single core and vectorized operations. Unless you are doing high resolution computer vision in real time... You can still get away with the single core even then, but that requires some very smart sublinear processing.
- ibrahima 11y ago
  Completely right. Applying a neural network is much faster than training one. The main trick here is fitting the trained model into cache (or smaller) so that the matrix multiplies are fast.
jchomali 11y ago
Of course they are
ytdht 11y ago
they are awesome, but not that difficult to implement
amelius 11y ago
> this blog post made me feel like "those guys are doing black magic". Two remarks. First, these guys probably don't know very well why what they are doing works so well ;) It requires a lot of trial and error, and a lot of patience and a lot of compute power (the latter being the reason why we are seeing breakthroughs only now). Second, training a neural net requires different computing power from deploying the net. The neural network that is installed on your phone has been trained using a lot of time and/or a very large cluster. Your phone is merely "running" the network, and this requires much less compute power.

by motoboi 11y ago

I am 15 years into this computers thing and this blog post made me feel like "those guys are doing black magic". Neural networks and deep learning are truly awesome technologies.

deleted 11y ago

[deleted]

dr_zoidberg 11y ago

They are, but once you start learning about them, you realize the "black magic" part comes mostly from their mathematical nature and very little from them being "inteligent computers". A neural net is a graph, in which a subset of nodes are "inputs" (that's where the net gets information), some are outputs, and there are other nodes which are called "hidden neurons". The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net. For example I-H-O is a tipical feed forward net, in which I (inputs) is the input layer, H is the hidden layer and O the output layer. All the hidden neurons connect with all the input neurons "output", and all the output neurons connect to the hidden neurons "output". The connections are called "weights", and the training adjusts the weights of all the neuron with lots of cases until the desired output is achieved. There are also algorithms and criteria to stop before the net "learns too much" and looses the ability to generalize (this is called overfitting). In particular, a net with one hidden layer and one output layer is a universal function estimator -- that is, an estimator that can model any mathematical function of the form f(x1, x2, x3, ..., xn) = y. Deep learning means you're using a feedforward net with lots of hidden layers (I think it's usually between 5 to 15 now), which apply convolution operators (hence the "convolutional" in the name), and lots of neurons (in the order of thousands). All this was nearly impossible until the GPGPUs came along, because of the time it took to train a modest network (minutes to hours for a net with a between 50 to 150 neurons in one hidden layer). This is a very shortened explanation -- if you want to read more I recommend this link[1] which gives some simple Python code to illustrate and implement the innards of a basic neural network and you can learn from the inside. Once you get that you should move to more mature implementations, like Theano or Torch to get the full potential of neutral nets without worrying about implementation. [1] http://iamtrask.github.io/2015/07/12/basic-python-network/ http://iamtrask.github.io/2015/07/12/basic-python-network/

beambot 11y ago
You could say very much the same about the brain... > [...] the "black magic" part comes mostly from their mathematical nature and very little from them being "inteligent computers". A brain is a graph, in which a subset of neurons are "inputs", some are outputs, and others are "hidden". The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net. The deep question about deep learning is "Why is it so bloody effective?"
- dr_zoidberg 11y ago
  I work in the field, and while some models are based on biological structures/systems, there's a lot of fuzz about them being "based on biological foundations" that is now best avoided. Yes, it is true the model is based on them, but it's a model that only covers very little of the real complexity. So in a sense, it's naive to say "put a billion neurons in there and you'll get a rat brain" (as was publicized one time). The effectiveness comes from their non-linear nature and their ability to "learn" (store knowledge in the weights, that is derived from the training process). And black magic, of course!
- Lawtonfogle 11y ago
  If there is magic to be found, it may be in that question. Why about graphs (namely the subset that are deep neural networks) allow them to not only contain such powerful heuristics, but also allow them to be created from scratch with barely any knowledge of the problem domain. As a side note, I was playing a board game last night (Terra Mystica I believe) and wondering if you could get 5 different neural networks to play the game and then train them against each other (and once they are good enough, against players). I wonder how quickly one could train a network that is unbeatable by humans? Maybe even scale it up to training it to play multiple board games til it is really good at all of them before setting it lose on a brand new one (with a similar genre). Maybe Google could use this to make a Go bot. But what happens if this is used for evil instead? Say a neural network that reads a person's body language and determines how easily they can be intimidated by either a criminal or the government. Or one that is used to hunt down political dissidents. Imagine the first warrant to be signed by a judge for no reason other than a neural network saying the target is probably committing a crime...

dchichkov 11y ago

They are doing matrix multiplications. To pass input a single time through even some very large neural network - it is a relatively fast operation (if compared to training such a network, that is). Training requires data centers and arrays of GPUs. Passing the input through the network - usually you can get away with a single core and vectorized operations. Unless you are doing high resolution computer vision in real time... You can still get away with the single core even then, but that requires some very smart sublinear processing.

ibrahima 11y ago
Completely right. Applying a neural network is much faster than training one. The main trick here is fitting the trained model into cache (or smaller) so that the matrix multiplies are fast.

jchomali 11y ago

Of course they are

ytdht 11y ago

they are awesome, but not that difficult to implement

amelius 11y ago

> this blog post made me feel like "those guys are doing black magic". Two remarks. First, these guys probably don't know very well why what they are doing works so well ;) It requires a lot of trial and error, and a lot of patience and a lot of compute power (the latter being the reason why we are seeing breakthroughs only now). Second, training a neural net requires different computing power from deploying the net. The neural network that is installed on your phone has been trained using a lot of time and/or a very large cluster. Your phone is merely "running" the network, and this requires much less compute power.