Basics of Neural Networks with example codes and illustrations

Post by seanmcdirmid » Sat, Aug 31, 2013, 12:30 PM UTC

nomailing wrote: post #30

coderjack wrote: post #12

Earlier quoted context omitted.

Yes its true that neural nets are not used in commercial systems because its computation intensive and also needs huge infrastructure even for things like finding cat images out of youtube videos but the results are far more accurate then any of the statistical methods used in commercial systems. There is time-performance trade-off when it comes to choosing statistical methods over neural nets and also it's a field y…

Why do you think that they are not used in commercial systems? As far as I remember from leckeres of Geoffrey Hinton they are now used quite extensively in speech recognition systems on smartphones, etc.

they are used in speech recognition systems "for smartphones", not "on smartphones." Quite a big difference. None of these systems will work if your phone is not connected to a server that does the decoding work.

Post by catwell » Mon, Sep 02, 2013, 10:06 AM UTC

dave_sullivan wrote: post #16

JDDunn9 wrote: post #10

Does anyone have any examples of areas where neural networks beat out statistical based methods, other than maybe image recognition? I can't even think of another major area where they dominate. - Search engines use algorithms, not neural nets. - The most popular algorithm on Kaggle (data analysis competitions) is random forests - Google's self-driving car uses statistical-based methods I can't imagine commercial air…

Glad you asked... Definitely image recognition: http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf Speech recognition: http://www.cs.toronto.edu/~hinton/absps/RNN13.pdf Natural language processing: http://www.socher.org/index.php/DeepLearningTutorial/DeepLea... , http://aclweb.org/anthology/N/N13/N13-1090.pdf If you're into kaggle competitions: http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it... I don't…

Actually, in many cases non-NN methods still beat NN for those use cases, at least if you consider things like the speed of the response and consumption of resources as relevant.

Recent papers show that NN are coming back, but I think most of the speech recognition out there is still Hidden Markov Models and most of the image recognition is definitely based on tailored detectors & descriptors. This is especially true when you do these things on the users' mobile devices.

Re: Basics of Neural Networks with example codes and illustrations

Re: Basics of Neural Networks with example codes and illustrations