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  • My neural network gets "stuck" while training. Is this normal?

    - by Vivin Paliath
    I'm training a XOR neural network via back-propagation using stochastic gradient descent. The weights of the neural network are initialized to random values between -0.5 and 0.5. The neural network successfully trains itself around 80% of the time. However sometimes it gets "stuck" while backpropagating. By "stuck", I mean that I start seeing a decreasing rate of error correction. For example, during a successful training, the total error decreases rather quickly as the network learns, like so: ... ... Total error for this training set: 0.0010008071327708653 Total error for this training set: 0.001000750550254843 Total error for this training set: 0.001000693973929822 Total error for this training set: 0.0010006374037948094 Total error for this training set: 0.0010005808398488103 Total error for this training set: 0.0010005242820908169 Total error for this training set: 0.0010004677305198344 Total error for this training set: 0.0010004111851348654 Total error for this training set: 0.0010003546459349181 Total error for this training set: 0.0010002981129189812 Total error for this training set: 0.0010002415860860656 Total error for this training set: 0.0010001850654351723 Total error for this training set: 0.001000128550965301 Total error for this training set: 0.0010000720426754587 Total error for this training set: 0.0010000155405646494 Total error for this training set: 9.99959044631871E-4 Testing trained XOR neural network 0 XOR 0: 0.023956746649767453 0 XOR 1: 0.9736079194769579 1 XOR 0: 0.9735670067093437 1 XOR 1: 0.045068688874314006 However when it gets stuck, the total errors are decreasing, but it seems to be at a decreasing rate: ... ... Total error for this training set: 0.12325486644721295 Total error for this training set: 0.12325486642503929 Total error for this training set: 0.12325486640286581 Total error for this training set: 0.12325486638069229 Total error for this training set: 0.12325486635851894 Total error for this training set: 0.12325486633634561 Total error for this training set: 0.1232548663141723 Total error for this training set: 0.12325486629199914 Total error for this training set: 0.12325486626982587 Total error for this training set: 0.1232548662476525 Total error for this training set: 0.12325486622547954 Total error for this training set: 0.12325486620330656 Total error for this training set: 0.12325486618113349 Total error for this training set: 0.12325486615896045 Total error for this training set: 0.12325486613678775 Total error for this training set: 0.12325486611461482 Total error for this training set: 0.1232548660924418 Total error for this training set: 0.12325486607026936 Total error for this training set: 0.12325486604809655 Total error for this training set: 0.12325486602592373 Total error for this training set: 0.12325486600375107 Total error for this training set: 0.12325486598157878 Total error for this training set: 0.12325486595940628 Total error for this training set: 0.1232548659372337 Total error for this training set: 0.12325486591506139 Total error for this training set: 0.12325486589288918 Total error for this training set: 0.12325486587071677 Total error for this training set: 0.12325486584854453 While I was reading up on neural networks I came across a discussion on local minimas and global minimas and how neural networks don't really "know" which minima its supposed to be going towards. Is my network getting stuck in a local minima instead of a global minima?

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  • How to create a backpropagation neural network in neurondonet?

    - by Suraj Prakash
    I am doing stock market prediction using ANNs in c#.net. I am using NeuronDotNet for the neural part. I have to give eight inputs to the network, with a hidden layer consisting 8 nodes and a single node output layer. Can anybody please give me some coding ideas for this???? This project was not a AI course assignment, but my major project. I have studied about the stocks and found various factors that affected the future value of stock of a company. Now I have to use these factors as input to the neural network. I am not getting into how to implement these factors in the neural network. I have just decided to use those eight factors as eight nodes in the input layer but things are going complex. My concern is to use these factors as input and train the neural network for output as next day's stock value. What major things should I have to care about??

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  • Do I need social networks to be an expert developer? [closed]

    - by Gerald Blizzy
    This question may sound odd, but do I need twitter, facebook and google+ if I am a web-developer? I see many expert developers nowadays using it in working order. It seems like it's harder to stay in touch with customers, co-workers and potential customers if you don' use social networks. Am I right? Reason why I ask is that I am totally not a facebook/twitter person, I find it boring and annoying. I understand that linkedin is usefull for career, but what about twitter and facebook? Are they needed for web-developer career? What I am trying to ask is if I only use linkedin, own portfolio website, google talks, gmail and something like github, would I actually miss anything professionally/job-wise? My thoughts are that I can just have my portfolio website where I list all my projects aswell as contacts page with my google talks/gmail account. It can suit both fulltime job, freelance and own projects. So this way email and google talks is just enough. Am I right or not? Thanks in advance!

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  • How do I mashup Google Maps with geolocated photos from one or more social networks?

    - by PureCognition
    I'm working on a proof of concept for a project, and I need to pin random photos to a Google Map. These photos can come from another social network, but need to be non-porn. I've done some research so far, Google's Image Search API is deprecated. So, one has to use the Custom Search API. A lot of the images aren't photos, and I'm not sure how well it handles geolocation yet. Twitter seems a little more well suited, except for the fact that people can post pictures of pretty much anything. I was also going to look into the API's for other networks such as Flickr, Picasa, Pinterest and Instagram. I know there are some aggregate services out there that might have done some of this mash-up work for me as well. If there is anyone out there that has a handle on social APIs and where I should look for this type of solution, I would really appreciate the help. Also, in cases where server-side implementation matters, I'm a .NET developer by experience.

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  • Social Technology and the Potential for Organic Business Networks

    - by Michael Snow
    Guest Blog Post by:  Michael Fauscette, IDCThere has been a lot of discussion around the topic of social business, or social enterprise, over the last few years. The concept of applying emerging technologies from the social Web, combined with changes in processes and culture, has the potential to provide benefits across the enterprise over a wide range of operations impacting employees, customers, partners and suppliers. Companies are using social tools to build out enterprise social networks that provide, among other things, a people-centric collaborative and knowledge sharing work environment which over time can breakdown organizational silos. On the outside of the business, social technology is adding new ways to support customers, market to prospects and customers, and even support the sales process. We’re also seeing new ways of connecting partners to the business that increases collaboration and innovation. All of the new "connectivity" is, I think, leading businesses to a business model built around the concept of the network or ecosystem instead of the old "stand-by-yourself" approach. So, if you think about businesses as networks in the context of all of the other technical and cultural change factors that we're seeing in the new information economy, you can start to see that there’s a lot of potential for co-innovation and collaboration that was very difficult to arrange before. This networked business model, or what I've started to call “organic business networks,” is the business model of the information economy.The word “organic” could be confusing, but when I use it in this context, I’m thinking it has similar traits to organic computing. Organic computing is a computing system that is self-optimizing, self-healing, self-configuring, and self-protecting. More broadly, organic models are generally patterns and methods found in living systems used as a metaphor for non-living systems.Applying an organic model, organic business networks are networks that represent the interconnectedness of the emerging information business environment. Organic business networks connect people, data/information, content, and IT systems in a flexible, self-optimizing, self-healing, self-configuring, and self-protecting system. People are the primary nodes of the network, but the other nodes — data, content, and applications/systems — are no less important.A business built around the organic business network business model would incorporate the characteristics of a social business, but go beyond the basics—i.e., use social business as the operational paradigm, but also use organic business networks as the mode of operating the business. The two concepts complement each other: social business is the “what,” and the organic business network is the “how.”An organic business network lets the business work go outside of traditional organizational boundaries and become the continuously adapting implementation of an optimized business strategy. Value creation can move to the optimal point in the network, depending on strategic influencers such as the economy, market dynamics, customer behavior, prospect behavior, partner behavior and needs, supply-chain dynamics, predictive business outcomes, etc.An organic business network driven company is the antithesis of a hierarchical, rigid, reactive, process-constrained, and siloed organization. Instead, the business can adapt to changing conditions, leverage assets effectively, and thrive in a hyper-connected, global competitive, information-driven environment.To hear more on this topic – I’ll be presenting in the next webcast of the Oracle Social Business Thought Leader Webcast Series - “Organic Business Networks: Doing Business in a Hyper-Connected World” this coming Thursday, June 21, 2012, 10:00 AM PDT – Register here

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  • Problems with real-valued input deep belief networks (of RBMs)

    - by Junier
    I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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  • Problems with real-valued deep belief networks (of RBMs)

    - by Junier
    I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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  • Designing bayesian networks

    - by devoured elysium
    I have a basic question about Bayesian networks. Let's assume we have an engine, that with 1/3 probability can stop working. I'll call this variable ENGINE. If it stops working, then your car doesn't work. If the engine is working, then your car will work 99% of the time. I'll call this one CAR. Now, if your car is old(OLD), instead of not working 1/3 of the time, your engine will stop working 1/2 of the time. I'm being asked to first design the network and then assign all the conditional probabilities associated with the table. I'd say the diagram of this network would be something like OLD -> ENGINE -> CAR Now, for the conditional probabilities tables I did the following: OLD |ENGINE ------------ True | 0.50 False | 0.33 and ENGINE|CAR ------------ True | 0.99 False | 0.00 Now, I am having trouble about how to define the probabilities of OLD. In my point of view, old is not something that has a CAUSE relationship with ENGINE, I'd say it is more a characteristic of it. Maybe there is a different way to express this in the diagram? If the diagram is indeed correct, how would I go to make the tables? Thanks

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  • Neural network input data, cartesian plane x/y coordinates, correlate with Handwriting.

    - by Sam
    I very curious about making a handwriting recognition application in a web browser. Users draw a letter, ajax sends the data to the server, neural network finds the closest match, and returns results. So if you draw an a, the first result should be an a, then o, then e, something like that. I'm don't know much about neural networks. What kinda data would I need to pass to the NN. Could it be an array of the x/y coordinates where the user has drawn on a pad. Or what type of data is the neural network expecting or would produce the best results for handwriting?

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  • What problems have you solved using artificial neural networks?

    - by knorv
    I'd like to know about specific problems you - the SO reader - have solved using artificial neural network techniques and what libraries/frameworks you used if you didn't roll your own. Questions: What problems have you used artificial neural networks to solve? What libraries/frameworks did you use? I'm looking for first-hand experiences, so please do not answer unless you have that.

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  • Introduction to the SQL Server Analysis Services Neural Network Data Mining Algorithm

    In data mining and machine learning circles, the neural network is one of the most difficult algorithms to explain. Fortunately, SQL Server Analysis Services allows for a simple implementation of the algorithm for data analytics. Dallas Snider explains 24% of devs don’t use database source control – make sure you aren’t one of themVersion control is standard for application code, but databases haven’t caught up. So what steps can you take to put your SQL databases under version control? Why should you start doing it? Read more to find out…

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  • Artificial Neural Networks

    - by user1724140
    I have an Artificial Networks which needs to recognize 130 different types of moves encoded in terms of 1s and 0s. Therefore the number of outputs I used is 8 so that all my patterns could be distinguished. However, by using 8 outputs, the different types of patterns possible is 256, leaving me with 126 different types of patterns useless. Do these extra 126 different patterns ruin my ANN's ability? Is there a better way not to have these unused holes?

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  • Data Networks Visualized via Light Paintings [Video]

    - by ETC
    All around you are wireless data networks: cellular networks, Wi-Fi networks, a world of wireless communication. Check out this awesome video of network signals mapped over a cityscape. What would happen if you made a device that allowed you to map signal strength onto film? In the following video electronics tinkerers craft an LED meter and use it to paint onto long exposure photographs with phenomenal results. Immaterials: light painting Wi-Fi [via Make] Latest Features How-To Geek ETC Learn To Adjust Contrast Like a Pro in Photoshop, GIMP, and Paint.NET Have You Ever Wondered How Your Operating System Got Its Name? Should You Delete Windows 7 Service Pack Backup Files to Save Space? What Can Super Mario Teach Us About Graphics Technology? Windows 7 Service Pack 1 is Released: But Should You Install It? How To Make Hundreds of Complex Photo Edits in Seconds With Photoshop Actions Add a “Textmate Style” Lightweight Text Editor with Dropbox Syncing to Chrome and Iron Is the Forcefield Really On or Not? [Star Wars Parody Video] Google Updates Picasa Web Albums; Emphasis on Sharing and Showcasing Uwall.tv Turns YouTube into a Video Jukebox Early Morning Sunrise at the Beach Wallpaper Data Networks Visualized via Light Paintings [Video]

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  • Are evolutionary algorithms and neural networks used in the same problem domains?

    - by Joe Holloway
    I am trying to get a feel for the difference between the various classes of machine-learning algorithms. I understand that the implementations of evolutionary algorithms are quite different from the implementations of neural networks. However, they both seem to be geared at determining a correlation between inputs and outputs from a potentially noisy set of training/historical data. From a qualitative perspective, are there problem domains that are better targets for neural networks as opposed to evolutionary algorithms? I've skimmed some articles that suggest using them in a complementary fashion. Is there a decent example of a use case for that? Thanks

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  • Organic Business Networks -Don't Miss This Webcast!

    - by Michael Snow
    TUNE IN TODAY!! Oracle Social Business Thought Leaders Webcast Series Thursday, June 21st 10am PST  Organic Business Networks: Doing Business in a Hyper-Connected World Organic business networks connect people, data, content, and IT systems in a flexible, self-optimizing, self-healing, self-configuring and self-protecting system. Join us for this webcast and hear examples of how businesses today can effectively utilize the interconnectedness of emerging business information environments, adapt to changing conditions, and leverage assets effectively to thrive in a hyper-connected, globally competitive, information driven world. Listen as Featured Speaker, Michael Fauscette, GVP, Software Business Solutions, IDC, discusses: Emerging trends in social business that are driving transformative changes today The dynamic characteristics that make up social, collaborative, and connected enterprises Effective ways that technology combined with culture and process provide unique competitive advantage through new organic networked business models. Register now for the fifth Webcast in the Social Business Thought Leaders Series,“Organic Business Networks: Doing Business in a Hyper-Connected World.”

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  • Application to organize / manage installed networks

    - by vicmp3
    I was wondering if there is a Application where you can organize networks. I mean if you have installed some networks you have to note every pc's name, his ip-address and so on. Is there a Application where you can manage it? I saw the monitoring tools but that is not exactly what I'm looking for. Maybe I didnt explain me well, after all my englis his not so good :) For example if I install many different networks I write in a book how I configured them. I write pc-name ip-address ip-gateway ip-broadcast and so on for each network. It will be great if I can do it in a program to organize it well, and for example it gives me a node view of the network.

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  • How do you save a Neural Network to file using Ruby's ai4r gem?

    - by Jaime Bellmyer
    I'm using ruby's ai4r gem, building a neural network. Version 1.1 of the gem allowed me to simply do a Marshal.dump(network) to a file, and I could load the network back up whenever I wanted. With version 1.9 a couple years later, I'm no longer able to do this. It generates this error when I try: no marshal_dump is defined for class Proc I know the reason for the error - Marshal can't handle procs in an object. Fair enough. So is there something built in to ai4r? I've been searching with no luck. I can't imagine any practical use for a neural network you have to rebuild from scratch every time you want to use it.

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  • Hyper-V for Developers Part 1 Internal Networks

    Over the last year, weve been working with Microsoft to build training and demo content for the next version of Office Communications Server code-named Microsoft Communications Server 14.  This involved building multi-server demo environments in Hyper-V, getting them running on demo servers which we took to TechEd, PDC, and other training events, and sometimes connecting the demo servers to the show networks at those events.  ITPro stuff that should scare the hell out of a developer! It can get ugly when I occasionally have to venture into ITPro land.  Lets leave it at that. Having gone through this process about 10 to 15 times in the last year, I finally have it down.  This blog series is my attempt to put all that knowledge in one place if anything, so I can find it somewhere when I need it again.  Ill start with the most simple scenario and then build on top of it in future blog posts. If youre an ITPro, please resist the urge to laugh at how trivial this is. Internal Hyper-V Networks Lets start simple.  An internal network is one that intended only for the virtual machines that are going to be on that network it enables them to communicate with each other. Create an Internal Network On your host machine, fire up the Hyper-V Manager and click the Virtual Network Manager in the Actions panel. Select Internal and leave all the other default values. Give the virtual network a name, and leave all the other default values. After the virtual network is created, open the Network and Sharing Center and click Change Adapter Settings to see the list of network connections. The only thing I recommend that you do is to give this connection a friendly label, e.g. Hyper-V Internal.  When you have multiple networks and virtual networks on the host machines, this helps group the networks so you can easily differentiate them from each other.  Otherwise, dont touch it, only bad things can happen. Connect the Virtual Machines to the Internal Network Im assuming that you have more than 1 virtual machine already configured in Hyper-V, for example a Domain Controller, and Exchange Server, and a SharePoint Server. What you need to do is basically plug in the network to the virtual machine.  In order to do this, the machine needs to have a virtual network adapter.  If the VM doesnt have a network adapter, open the VMs Settings and click Add Hardware in the left pane.  Choose the virtual network to which to bind the adapter to. If you already have a virtual network adapter on the VM, simply connect it to the virtual network. Assign IP Addresses to the Virtual Machines on the Internal Network Open the Network and Sharing Center on your VM, there should only be 1 network at this time.  Open the Properties of the connection, select Internet Protocol Version 4 (TCP/IPv4) and hit Properties. In this environment, Im assigning IP addresses as 192.168.0.xxx.  This particular VM has an IP address of 192.168.0.40 with a subnet mask of 255.255.255.0, and a DNS Server of 192.168.0.18.  DNS is running on the Domain Controller VM which has an IP address of 192.168.0.18. Repeat this process on every VM in your environment, obviously assigning a unique IP address to each.  In an environment with a domain controller, you should now be able to ping the machines from each other. What Next? After completing this process, heres what you still cannot do: Access the internet from any of the VMs Remote desktop to a VM from the host Remote desktop to a VM over the network In the next post, well take a look configuring an External network adapter on the virtual machines.  Well then build on top of that so that you can RDP into the VMs from the host machine and over the network.Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • How can I prioritize wireless networks with network-manager

    - by kynan
    Mostly I use my laptop in an environment where different wireless networks are available and I would like to preferably connect to one of those and only fall back to the other one if that one is not available. Is there any way of prioritizing which wireless networks network-manager preferably connects to? The only workaround I found so far was unchecking Connect Automatically in the options and re-enabling it when my preferred network is not available.

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  • Problem connecting to certain wifi networks

    - by Romas
    I'm using lenovo u300s (it has Intel Centrino Wireless-N 1030 card), and I can connect to wireless network at home with no problem, but I can not connect to any networks at my university. I'm not sure if it's the case but most of university networks are open and you need to sign up your username as you enter browser. There is also an eudoroam network, but I can not connect to it either. Any suggestions? Yes I have the latest ubuntu version.

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