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Artificial Neural Networks for Beginners

blogs.mathworks.com

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Re: Artificial Neural Networks for Beginners

#41

Post by tacos »

nileshtrivedi wrote: post #22
tacos wrote: post #14

Earlier quoted context omitted.

Matlab vs Octave is one of those places where it's still worth buying the real one. Personal use license (including machine learning toolkit) is under $200. If you're not willing to spend $200 to learn something you're probably not that interested. Browse JSTOR at the library versus Googling any historical, scientific or research topic and you'll quickly learn that "internet" offers the shitty version of surprisingly…

> If you're not willing to spend $200 to learn something you're probably not that interested. This is a bit presumptuous. I know people for which 200$ is a month's income. If you only consider their disposable income, 200$ would probably take 6 months.

Maybe that's why there's the $49 student license and the $99 one that includes ten toolboxes including machine learning.

Re: Artificial Neural Networks for Beginners

#42

Post by tacos »

MadManE wrote: post #36
tacos wrote: post #15

Earlier quoted context omitted.

The guy cutting my lawn spent more on his tools than MATLAB costs.

I also spent more on video games in the last year than it costs. What's your point? That it's so cheap he should just buy it, even if there are better, cheaper tools out there? That seems like a waste to me.

I was refuting this silly argument: "Good luck once you're out of university and want to start your own thing, you won't be able to afford it."

Yup, startups have costs. Go figure. And sometimes you get what you pay for.

I wish I'd learned Matlab sooner. I still love the Python ecosystem, but Matlab's replaced a LOT of dicking around in Python for me. It does completely different things, and certain things are trivial or impossible in each place. Worth learning both.

Re: Artificial Neural Networks for Beginners

#43

Post by juliendorra »

tacos wrote: post #15
fphilipe wrote: post #12

Earlier quoted context omitted.

At times my mechanical engineering courses at university felt like MATLAB tutorials. You had to use it, no way around it. Good luck once you're out of university and want to start your own thing, you won't be able to afford it. The computer science courses in contrast preferred open source tools over proprietary ones.

The guy cutting my lawn spent more on his tools than MATLAB costs.

Of course it's not only about the price. Open and free tools are important for reproducibility of research, including computer science research, economic research, etc. Having open access articles/papers, we are now moving to open availability of data for reproducibility of results. Open tools are the third component, allowing a complete reproducibility of research, unencumbered by arbitrary lock-in. It is then important that we teach and share open and free tool with science learners, so that as they progress they can freely share fully reproducible research, with anyone in the world.

[edit] in summary, in the age of open access, FLOSS science tools become a must -- and a collective responsibility.

Re: Artificial Neural Networks for Beginners

#44

Post by moron4hire »

curiousjorge wrote: post #37

can someone give me some real world business need where I can apply RNN and this type of knowledge? Obviously not looking for a hand out but open to exploring problems in the enterprise or any other potential problems worth solving which has a market. I find that having a goal of what I want to solve or create motivates me to learn. Whereas if I'm studying Statistics but don't have a clear goal that motivates me (cal…

This is a pretty good list.

https://en.wikipedia.org/wiki/Artificial_neural_network#Appl...

Re: Artificial Neural Networks for Beginners

#45

Post by tacos »

juliendorra wrote: post #43
tacos wrote: post #15

Earlier quoted context omitted.

The guy cutting my lawn spent more on his tools than MATLAB costs.

Of course it's not only about the price. Open and free tools are important for reproducibility of research, including computer science research, economic research, etc. Having open access articles/papers, we are now moving to open availability of data for reproducibility of results. Open tools are the third component, allowing a complete reproducibility of research, unencumbered by arbitrary lock-in. It is then impor…

The most important thing is giving brilliant minds the best tools, period.

If those tools make it impossible to share data, or publish results reproducibly, then I'd agree that those tools suck. However Matlab reads and writes every damn format under the sun.

Don't confuse "create, invent and build" with "export and publish." They're fundamentally different tasks.

Re: Artificial Neural Networks for Beginners

#46

Post by strong_ai »

moron4hire wrote: post #27

ANNs are great for the right application. But I'm starting to fear "Deep Learning" is the new "Big Data" buzzword. I believe ANNs are Turing Complete, meaning they should be able to compute anything (EDIT: + "that is computable by any other Turing Machine"). The questions are, can a training regimen be created to create the right ANN to solve "any" problem, and if so, is it an efficient means to solve that problem? F…

ANNs have been proven to be universal approximators (https://en.wikipedia.org/wiki/Universal_approximation_theore...) which I think is what you meant when you said 'Turing Complete'.

Re: Artificial Neural Networks for Beginners

#47

Post by moron4hire »

strong_ai wrote: post #46
moron4hire wrote: post #27

ANNs are great for the right application. But I'm starting to fear "Deep Learning" is the new "Big Data" buzzword. I believe ANNs are Turing Complete, meaning they should be able to compute anything (EDIT: + "that is computable by any other Turing Machine"). The questions are, can a training regimen be created to create the right ANN to solve "any" problem, and if so, is it an efficient means to solve that problem? F…

ANNs have been proven to be universal approximators ( https://en.wikipedia.org/wiki/Universal_approximation_theore... ) which I think is what you meant when you said 'Turing Complete'.

There is also "Turing Computability With Neural Nets" (Seigelmann, Sontag, 1991 http://www.sciencedirect.com/science/article/pii/08939659919...)

    This paper shows the existence of a finite neural network, made up of sigmoidal 
    neurons, which simulates a universal Turing machine. It is composed of less than
    10^5 synchronously evolving processors, interconnected linearly. High-order
    connections are not required.

Re: Artificial Neural Networks for Beginners

#48

Post by Kenji »

ramgorur wrote: post #40

It takes forever to run a simple patternsearch(), fmincon() if a function gets a bit complicated. Their mcc compiler is even more crappier, it has so many memory leaks that even valgrind gives up and gets freezed. I do not want to run a MATALBBED-ANN over large datasets, no way. MATLAB scwhag: "Do you speak MATLAB ?" me: "No, I don't speak MATLAB, and I don't want to"

I love matlab but specifically with neural networks, I made bad experiences. Just generally subpar performance on convergence speed and results. It's better to use caffe, which is the best neural network kit I know. Also, large parts of caffe are being implemented for GPUs such that performance becomes even better.

Re: Artificial Neural Networks for Beginners

#49

Post by rsy96 »

moron4hire wrote: post #27

ANNs are great for the right application. But I'm starting to fear "Deep Learning" is the new "Big Data" buzzword. I believe ANNs are Turing Complete, meaning they should be able to compute anything (EDIT: + "that is computable by any other Turing Machine"). The questions are, can a training regimen be created to create the right ANN to solve "any" problem, and if so, is it an efficient means to solve that problem? F…

> But I'm starting to fear "Deep Learning" is the new "Big Data" buzzword.

You spent several paragraph criticizing ANNs, but regular ANNs are not deep learning at all.

Re: Artificial Neural Networks for Beginners

#50

Post by moron4hire »

rsy96 wrote: post #49
moron4hire wrote: post #27

ANNs are great for the right application. But I'm starting to fear "Deep Learning" is the new "Big Data" buzzword. I believe ANNs are Turing Complete, meaning they should be able to compute anything (EDIT: + "that is computable by any other Turing Machine"). The questions are, can a training regimen be created to create the right ANN to solve "any" problem, and if so, is it an efficient means to solve that problem? F…

> But I'm starting to fear "Deep Learning" is the new "Big Data" buzzword. You spent several paragraph criticizing ANNs, but regular ANNs are not deep learning at all.

I didn't criticize ANN, I criticized perception of ANNs and learning algorithms as general purpose solutions.

Also, I have no idea what you're talking about that ANNs are not involved with deep learning. Recurrent and Convolution Networks are types of ANN. https://en.wikipedia.org/wiki/Deep_learning

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