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Artificial Neural Networks for Beginners

• deleted 11y ago
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• ching_wow_ka 11y ago
  If you're trying to learn about deep learning, I highly suggest using Python(Theano) or Lua(Torch). They're free and used by the experts in the field for research. Even if you don't want to use the frameworks, you'll still have access to fast linear algebra routines.
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  • raphaelj 11y ago
    Could someone can recommend me a book about deep learning and/or machine learning for this kind of open-source library ? I do not have any background in ML nor DL.
    • wodenokoto 11y ago
      Then you might actually want to start in Matlab / Octave with Mchael Ng's coursera course on ML.
      • criddell 11y ago
        I think you are talking about Andrew Ng's course. I completed it and can't recommend it more highly. It is a really excellent, dense course and Ng is a very good teacher. https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning
        • artmageddon 11y ago
          I've completed the course as well - have you used any of the knowledge from it on anything in particular after you completed the course?
          • zardo 11y ago
            I'm taking the Coursera course right now. The course page at Stanford has a lot of student projects. The breadth of applications is pretty huge, definitely worth a check if you're looking for an idea. http://cs229.stanford.edu http://cs229.stanford.edu
        • joshz 11y ago
          Geoffrey Hinton's archived course is all about neural nets, I think you can enroll in the archived version, no code, just theory. https://www.coursera.org/course/neuralnets https://www.coursera.org/course/neuralnets
      • brudgers 11y ago
        I am working through Ng's course currently. It is hitting the right tones against my mathsephobia...keeping me constantly in that state of semiunderstanding that is intuition, a term Ng uses often. His choice of Octave/MatLab simplifies issues of dependencies. In particular the soft ones of documentation and community. This is something a lot of academic contexts get wrong with software: the tools are either to open ended and students wind up manipulating matrices with forloops or there's an inflexible stack of professional tools that require massive effort to learn and an orthogonal community or there is a toy IDE based on a senior thesis. Octave more or less follows the Unix philosophy of doing one thing and thus can meet many people where they are rather than with a one true way.
    • rsy96 11y ago
      Deep learning is pretty much a field still being rapidly advanced by research. A book on it would become obsolete the day it is published.
    • crazypyro 11y ago
      Although this is not for beginners of machine learning (learn that first), this is a book on deep learning that is currently in pre-publication and its being written by some big names in the field. http://www.iro.umontreal.ca/~bengioy/dlbook/ http://www.iro.umontreal.ca/~bengioy/dlbook/
    • nileshtrivedi 11y ago
      NVidia has a free online course going on covering these libraries: https://developer.nvidia.com/deep-learning-courses https://developer.nvidia.com/deep-learning-courses
    • obsim 11y ago
      Many great material of using Torch to do machine learning and deep learning from this Oxford course: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...
    • physicistjedi 11y ago
      I think the best introductory resources are Nielsen's book [http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/] and Hinton's online course [https://www.coursera.org/course/neuralnets https://www.coursera.org/course/neuralnets]. If you need something specifically for Theano, they have their own tutorial [http://deeplearning.net/tutorial/ http://deeplearning.net/tutorial/].
  • temuze 11y ago
    Theano is great! The learning curve can be a little difficult, but once it "clicks", it's nice to work with.
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• omegote 11y ago
  Matlab? Thanks, but no thanks.
  • artmageddon 11y ago
    There's also Octave [0] [0]: https://www.gnu.org/software/octave/ https://www.gnu.org/software/octave/
    • tacos 11y ago
      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 many things. (Shh... it's a secret.)
      • nileshtrivedi 11y ago
        > 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.
        • tacos 11y ago
          Maybe that's why there's the $49 student license and the $99 one that includes ten toolboxes including machine learning.
      • artmageddon 11y ago
        I'd agree that Matlab is great and I honestly thought Matlab would be more expensive. I've done Andrew Ng's class, and I learned a great deal from it. Given that I haven't really figured out what I'd like to do with what I learned from the class I'd prefer to stick with the "lite" math package first till I decide to get more serious.
• LesZedCB 11y ago
  Cached version since the database seems to be having issues http://webcache.googleusercontent.com/search?q=cache:UhEgP6_8fB4J:blogs.mathworks.com/loren/2015/08/04/artificial-neural-networks-for-beginners/+&cd=1&hl=en&ct=clnk&gl=us http://webcache.googleusercontent.com/search?q=cache:UhEgP6_...
• an4rchy 11y ago
  Nice article. If anyone is interested in understanding the theory and also dig deeper, the machine learning course on Coursera is a great place to start as well.
• gansai 11y ago
  Just got 'Database Error'while trying to connect to this page. Error establishing a database connection. So, the number of connections to this page is limited? or what could be the issues throwing this kind of error?
  • meesles 11y ago
    Just refresh a few times, the sites probably getting hammered with HN users
• akshayB 11y ago
  Are there any good Neural Network frameworks written in Ruby? The ones I have used (ruby-fann and AI4r) dramatically slow down when you use them on large amount of data.
• moron4hire 11y ago
  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? For example, it's fairly trivial to build an ANN to spit out the right results for a given polynomial function, i.e. "f(x, y, z) = ax + by + cz". Knowing the polynomial function ahead of time, you just generate a ton of input/output sets and feed them into the training of the ANN, and then from there on the ANN will spit them back out. The problem with that is, you didn't learn anything new. You didn't learn how to solve a new problem. It's somewhat useful for teaching people how to program ANNs, but I personally think it's garbage for teaching how to understand ANNs. ANNs make more sense when we already have the training data, but we don't know the underlying function that maps input to said outputs. In the trivial case of the polynomial function, if someone were to hand us the training set, we could use an ANN to figure out what the polynomial must be. Except--for this particular example of a polynomial function--this isn't very efficient. For a polynomial of N terms, you only need N+1 sets of IO to trivially use algebra to determine the function. You can use any of the readily available linear algebra libraries to do such a thing. In fact, I wrote a project for a client that does just that: it uses a basic matrix library to crunch a set of GPS data to create a quadratic formula estimation of curves in roads, so that model can them be resampled, continuously, sans noise. And if that function is not just a simple polynomial--if, say, it includes sines and cosines and square roots, etc.-- then the ANN is going to have to be large enough to include in it ad-hoc, arithmetic estimations of sine and cosine and square roots sufficient to give the right answers. It might even include several different estimating functions just for sine just because our mystery function requires more than one sine operation. It might even have corner cases where it gets the answer wrong, because you didn't have a sufficiently large data set for it to "figure out" things like the fact that sin(x) is approximately x for small values of x. If one knew the right formula (and yes, that's a big if), it'd be significantly more efficient to write a program that computed the values correctly. All of this is not to poo-poo on ANNs. ANNs are great tools for when we don't know the function and when the function is sufficiently non-trivial to discover. The polynomial example is like trying to kill a fly on the wall with a swarm of nanonmachines designed to evolve and learn how to construct a flyswatter (which is part of the reason I dislike it as a learning tool). But write traditional code to do Optical Character Recognition, I dare you. ANNs are just highly specialized. Think of setting up your ANN like defining the full width and depth of the space of all possible programs that you'd like to search for the program that solves your problem. You then use feedback to "walk" across that space until you find something that looks like your desired program. We're entering an era where we have the memory and distributed processing capabilities to crank out some rather large ANNs. For some problems, we end up training a computer to write programs for us that we could have written on our own. This can impact the number of requests you can handle in a given amount of time. Of course, that is not necessarily bad, either. "Throwing money at the problem" is not the wrong solution when you have a lot more money than time. Technology is supposed to serve us, not the other way around. Why spend a week discovering a formula to map your data when you can train an ANN in a few hours? And perhaps you don't have very high requirements for request handling. Maybe you only need to process one image a minute on your particular system. Have at it. But you really, really need to know that is the case before you jump on the ANN bandwagon. You have to know what you want out of the ANN. If you don't have that ability to look at a set of inputs and express a desired set of outputs, then ANN isn't magic pixie dust that will solve that for you. If you have experts in your particular field telling you that your particular problem cannot be easily modeled, then ANNs might be helpful for you. If you are new to your field and you think "let's try an ANN", you're probably going to have a bad time. If you end up with an ANN that is estimating a relatively trivial program, and you're trying to provide a SaaS offering that is meant to scale to thousands or millions of concurrent users, the ANN approach could seriously harm your ability to scale.
• nnat425 11y ago
  awesome stuff
• fsqcds 11y ago
  I got this error for line "targetsd = dummyvar(targets);": Undefined function 'dummyvar' for input arguments of type 'double'.
• anacleto 11y ago
  Love this kind of post. Thank you. If you instead want to get a full understanding od Amazon Machine Learning, then this should be your go-to course: https://cloudacademy.com/amazon-web-services/courses/amazon-machine-learning/ https://cloudacademy.com/amazon-web-services/courses/amazon-... My $0.2cent
• curiousjorge 11y ago
  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 (calculating sports betting odds) then it's that much harder to master and appreciate it's applications. I guess to me, knowing the application of something before I dive both feet into learning it is actually the most important truth for beginners. As a kid, did you want to make video games and then ended up learning programming but ultimately not making video games? No 8 year old thinks I'm going to implement lxml in javascript one day they just think of something they like or curious about (ex. video games).
  • moron4hire 11y ago
    This is a pretty good list. https://en.wikipedia.org/wiki/Artificial_neural_network#Applications https://en.wikipedia.org/wiki/Artificial_neural_network#Appl...
• ramgorur 11y ago
  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"
  • Kenji 11y ago
    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.