How to Create Convolutional Neural Networks Using Java and DL4J

How to Create Convolutional Neural Networks Using #Java and @deeplearning4j


  • Using Deeplearning4J, you can create convolutional neural networks, also referred to as CNNs or ConvNets, in just a few lines of code.
  • If you don’t know what a CNN is, for now, just think of it as a feed-forward neural network that is optimized for tasks such as image classification and natural language processing.
  • If you want to list all the labels present in the dataset, you can use the following code:

    At this point, if you compile and run your project, you should see the following output:

    It’s now time to start creating the individual layers of our neural network.

  • Another important thing to note in the above code is the call to the method, which specifies that our neural network’s input type is convolutional, with 32×32 images having 3 colors.
  • To start training the convolutional neural network you just created, just call its method and pass the iterator object to it.

In this tutorial, you’ll learn how to use Java and DeepLearning4J(DL4J) to create a convolutional neural network that can classify CIFAR-10 images.
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Don’t use deep learning your data isn’t that big · Simply Statistics

Don't use deep learning your data isn't that big

  • Don’t use deep learning your data isn’t that big

    The other day Brian was at a National Academies meeting and he gave one of his usual classic quotes:

    When I saw that quote I was reminded of the blog post Don’t use hadoop – your data isn’t that big.

  • Just as with Hadoop at the time that post was written – deep learning has achieved a level of mania and hype that means people are trying it for every problem.
  • The issue is that only a very few places actually have the data to do deep learning.
  • But I’ve always thought that the major advantage of using deep learning over simpler models is that if you have a massive amount of data you can fit a massive number of parameters.
  • If you are Google, Amazon, or Facebook and have near infinite data it makes sense to deep learn.

Best quote from NAS DS Round Table: “I mean, do we need deep learning to analyze 30 subjects?” – B Caffo @simplystats #datascienceinreallife
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Cars are learning to hear like humans and self-diagnose problems

Hear that? Your #car just did - ! 🚗 #iot #ai

  • Just like humans identify whether or not there is a problem with the car by hearing odd sounds coming from the engine or by hearing creaking sounds, cars can identify as well.
  • Their software can give cars the ability to diagnose problems themselves by simply hearing anomalous sounds coming from the car and/or the environment.
  • They are testing the tech by providing it with many sounds that come from their cars.
  • This can be a huge boost as one of the important factors of customer dissatisfaction is odd noises coming from a car and not being able to locate the problem.
  • Other than diagnosing problems, this tech can also be used to help cars in perceiving their environment.

Intelligent machines are not only trying to learn and act like humans, they are beginning to sense their environment like them as well. Latest among them are cars that are getting a sense of hearing that would allow them to enhance their intelligence level by perceiving their environment like humans do.
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Artificial synapse could be key to brain-like computing

Artificial synapse could be key to brain-like computing  #ai

  • It behaves like a transistor, with one terminal regulating the electricity flowing between two others.
  • While it’s not exactly natural, it’s largely made out of carbon and hydrogen, and should be compatible with a real brain’s chemistry — the voltages are even the same as those that go through real neurons.The ultimate aim is to create neural networks that exhibit more of the properties of their fleshy equivalents, and they’ve achieved some degree of success.
  • There’s only one synapse so far, but the team has shown that a simulated array of them could accomplish real computing tasks with a high degree of accuracy: the network could recognize handwritten numbers after training on three data sets.
  • The biggest challenge is shrinking the synapse so that it achieves true synapse-like efficiency (they’re still using 10,000 times more energy than a real synapse needs to fire).
  • If scientists can get anywhere close to that, though, you could see neural networks that are not only low-power, but are safe enough to interact with real biology — think AI-driven implants.

If you’re going to craft brain-like computers, it stands to reason that you’d want to replicate brain-like behavior right down to the smallest elements, doesn’t…
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Machine Learning Used To Translate Bat Speech

Machine Learning Used To Translate Bat Speech  #ai

  • After that the information was fed into the computer and a machine learning algorithm was used to correctly identify which bat made which call.
  • 2016-07-12 Machine Learning Algorithm Could Be Used To Detect Depression
  • 2016-06-17 Scientists Create Algorithm To Help Predict Terrorist Attacks
  • Scientists believe that machine learning could be the key to understanding how animals talk.
  • So far the researchers are saying that the accuracy of their algorithm is around 71%, with an accuracy of 61% when trying to discern the argument, and 41% accuracy for the eventual outcome.

Have you ever wondered what your pet is saying? We know that sometimes they do certain actions or make a certain noise to indicate how they are…
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Smart auto-trading, artificial intelligence that ‘trades on the news’

The Robots Are Coming!!!  #artificialintelligence #roboadvisor #fintech #venturecapital

  • By using Stock Circles’ Service, registered users agree that there are inherent risks involved in trading securities.
  • Its stock screening Service is impersonal and not tailored to any specific individual’s needs.
  • Stock Circles does not promote the stocks featured in its Service, nor does it receives any compensation from Companies whose stocks appear in the Service and it has no financial interest in the outcome of any stock trades referenced in its Service.
  • Stock Circles Inc. (Stock Circles) provides a Service (the Service) that uses Artificial Intelligence concepts to screen and monitor stocks on an ongoing basis.

Stock Circles simplifies stock investing. Test artificial intelligence auto-trading in simulation mode today, risk free.
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Using Machine Learning to Detect Malicious URLs

#ICYMI Using #MachineLearning to Detect Malicious URLs  #security

  • Now that we have the data in our list, we have to vectorize our URLs.
  • KDnuggets Home > News > 2016 > Oct > Opinions, Interviews > Using Machine Learning to Detect Malicious URLs ( 16:n39 )
  • I wrote my own tokenizer function for this since URLs are not like some other document text.
  • Unfortunately or fortunately, there has been little work done on security with machine learning algorithms.
  • The data and code is available at Github .

This is a write-up of an experiment employing a machine learning model to identify malicious URLs. The author provides a link to the code for you to try yourself.

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BBC Radio 4

Could artificial intelligence replace judges and lawyers?

  • The BBC is not responsible for the content of external sites.
  • Lead researcher Dr Nikolaos Aletras, from University College London, told the Today programme the system would not spell the end of judges but could be used to prioritise cases most likely to involve human rights violations.
  • An artificial intelligence system has correctly predicted the verdicts of cases heard at the European Court of Human Rights, with a 79% accuracy.
  • (Photo: An AI figure posed with scales of justice.
  • Added, go to My Music to see full list.

A study has found that an AI could correctly predict the outcome of legal cases.
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