Do they still ask for your business idea when you register for their beta?
Okay, I should have worded that differently. There is also a paper of Salakhutdinov learning a kernel for Gaussian processes. That'd account for that as well. My point is (I did not really write that above) that deep…
Yes you are right. Still, deep learning has done nothing more than classification right now. What about predictive distributions, regression of complicated outputs (e.g. periodic data) and, most of all, heterogenous…
If an article says that Andrew Ng is "the man at the center of deep learning" it's just not right. Geoffrey Hinton's and Yoshua Bengio's impact were at least as high as his, if not far bigger.
If an article says that Andrew Ng is "the man at the center of deep learning" it's just not right. Geoffrey Hinton's and Yoshua Bengio's impact were at least as high as his, if not much higher.
This has been said about neural nets two times already. Sadly, they did never deliver. There are still applications where e.g. random forests beat the crap out of all kinds of deep learning algorithms in (a) training…
Wrong. Neural networks are affinity invariant. You can rotate, skew, translate the input data however you want, the optimum stays the same. Same for SVMs.
Here's why: In 95% of the cases you create a git branch, you check it out. In 95% of the cases you create a directory, you do not cd into it.
There is a machine learning that is not related to big data, you know. Many interesting problems in machine learning, and most of the hard ones, have a computational demand for which a single i7 and 16 GB of RAM are…
It's at least one way which has been advocated by leading researcher of the field. If you think differently, you should give references and explain what your AGI definition is.
With respect to Myo vs Leap. Tracking is much precise than EMG based posture estimation. Unless these guys made a leap of at least one order of magnitude compared to current state of the art, expect the LEAP to be buch…
To me it only looks like a limitation. So yeah, you can write a custom class. But you will need to add a custom "save state" and "load state" in your class' __iter__ method. So all the benefits go away.
If they could only be pickled...
ATLAS is a very bad name to pick; it's already taken when it comes to numerical data processing.
Not sure why this being downvoted. Provably friendly strong AI is actually sth that researcher care about, yet it is difficult to do because "friendly" has to be defined.
I really like the standard statistician stance "machine learning is basically just statistics". There is so much hate in it. :) The FAQ is full of this. For those who want to know the difference--and there are two--let…
Is the idea of organizing tasks for me depending on their length new? It feels awesome. Maybe this is the personal secreatry I have been waiting for all year.
Actually, you get it wrong. A Kernel function k(x, y) is a function that calculates phi(x)^T phi(y) for some phi. That means, it calculcates the dot product in a higher dimensional space of two data points; it does not…
I don't have any output. No url is returned. :( Idea sound pretty cool, I'd use it.
No. Deep learning is not about fixing the residuals of the current chain. Deep learning isn't even about residuals in the first place. It's about (1) finding good representations of your data (aka feature learning), (2)…
Yes you can. Check out the publications by Ciresan on MNIST, have a look at Hinton's dropout paper or at the Kaggle competition that used deep nets. Or try it yourself and spend a descent amount of time on hyper…
What exactly is wrong what I wrote? I did not say that all nets nowadays would be trained by RBMs (in the contrary, I said quite the opposite, that new algorithms had been developed). I just said that they were part of…
The breakthrough was the insight that while you cannot train a deep neural net at once with backprop, you can train one layer after the other greedily with an unsupervised objective and later fine tune it with standard…
Neural Nets cannot loop (unless they are recurrent neural nets) and are memory bound.
No. All currently used deep learning algorithms are special cases of neural networks. The reason why this is called "deep" learning is that before 2006, no one knew how to efficiently train neural nets with more than 1…
Do they still ask for your business idea when you register for their beta?
Okay, I should have worded that differently. There is also a paper of Salakhutdinov learning a kernel for Gaussian processes. That'd account for that as well. My point is (I did not really write that above) that deep…
Yes you are right. Still, deep learning has done nothing more than classification right now. What about predictive distributions, regression of complicated outputs (e.g. periodic data) and, most of all, heterogenous…
If an article says that Andrew Ng is "the man at the center of deep learning" it's just not right. Geoffrey Hinton's and Yoshua Bengio's impact were at least as high as his, if not far bigger.
If an article says that Andrew Ng is "the man at the center of deep learning" it's just not right. Geoffrey Hinton's and Yoshua Bengio's impact were at least as high as his, if not much higher.
This has been said about neural nets two times already. Sadly, they did never deliver. There are still applications where e.g. random forests beat the crap out of all kinds of deep learning algorithms in (a) training…
Wrong. Neural networks are affinity invariant. You can rotate, skew, translate the input data however you want, the optimum stays the same. Same for SVMs.
Here's why: In 95% of the cases you create a git branch, you check it out. In 95% of the cases you create a directory, you do not cd into it.
There is a machine learning that is not related to big data, you know. Many interesting problems in machine learning, and most of the hard ones, have a computational demand for which a single i7 and 16 GB of RAM are…
It's at least one way which has been advocated by leading researcher of the field. If you think differently, you should give references and explain what your AGI definition is.
With respect to Myo vs Leap. Tracking is much precise than EMG based posture estimation. Unless these guys made a leap of at least one order of magnitude compared to current state of the art, expect the LEAP to be buch…
To me it only looks like a limitation. So yeah, you can write a custom class. But you will need to add a custom "save state" and "load state" in your class' __iter__ method. So all the benefits go away.
If they could only be pickled...
ATLAS is a very bad name to pick; it's already taken when it comes to numerical data processing.
Not sure why this being downvoted. Provably friendly strong AI is actually sth that researcher care about, yet it is difficult to do because "friendly" has to be defined.
I really like the standard statistician stance "machine learning is basically just statistics". There is so much hate in it. :) The FAQ is full of this. For those who want to know the difference--and there are two--let…
Is the idea of organizing tasks for me depending on their length new? It feels awesome. Maybe this is the personal secreatry I have been waiting for all year.
Actually, you get it wrong. A Kernel function k(x, y) is a function that calculates phi(x)^T phi(y) for some phi. That means, it calculcates the dot product in a higher dimensional space of two data points; it does not…
I don't have any output. No url is returned. :( Idea sound pretty cool, I'd use it.
No. Deep learning is not about fixing the residuals of the current chain. Deep learning isn't even about residuals in the first place. It's about (1) finding good representations of your data (aka feature learning), (2)…
Yes you can. Check out the publications by Ciresan on MNIST, have a look at Hinton's dropout paper or at the Kaggle competition that used deep nets. Or try it yourself and spend a descent amount of time on hyper…
What exactly is wrong what I wrote? I did not say that all nets nowadays would be trained by RBMs (in the contrary, I said quite the opposite, that new algorithms had been developed). I just said that they were part of…
The breakthrough was the insight that while you cannot train a deep neural net at once with backprop, you can train one layer after the other greedily with an unsupervised objective and later fine tune it with standard…
Neural Nets cannot loop (unless they are recurrent neural nets) and are memory bound.
No. All currently used deep learning algorithms are special cases of neural networks. The reason why this is called "deep" learning is that before 2006, no one knew how to efficiently train neural nets with more than 1…