How to Create Convolutional Neural Networks Using Java and DL4J

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

#deeplearning

  • 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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How AI Helps Keep NASCAR Drivers Safe

Find out how #AI helps keep NASCAR drivers safe:  #GTC17

  • Bryan Goodman, an engineer with Argo AI / Ford Motor Company, spoke at the GPU Technology Conference last week about how his team applies deep learning originally built to develop self-driving cars to detect images of specific race cars.
  • Ford’s deep learning neural network was trained on a manual training set of thousands of images labeled by humans.
  • Goodman’s team suspected these were the items that the neural network prioritized in order to obtain such good results.
  • “Sometimes I hear people describe machine learning and, in particular, deep neural networks as a black box,” said Goodman.
  • The Pittsburgh-based Argo AI team is now working alongside the autonomous driving team at Ford.

When your race car is flying around the track at nearly 200 miles per hour, anything out of order —  even a candy wrapper stuck to the grill — can pose a danger to both car and driver.
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Accelerating open machine learning research with Cloud TPUs

  • Our goal is to ensure that the most promising researchers in the world have access to enough compute power to imagine, implement, and publish the next wave of ML breakthroughs.
  • We’re setting up a program to accept applications for access to the TensorFlow Research Cloud and will evaluate applications on a rolling basis.
  • The program will be highly selective since demand for ML compute is overwhelming, but we specifically encourage individuals with a wide range of backgrounds, affiliations, and interests to apply.
  • The program will start small and scale up.

Researchers need enormous computational resources to train the machine learning models that have delivered
recent advances in medical imaging, speech recognition, game playing, and many other domains. The TensorFlow
Research Cloud is a cluster of 1,000 Cloud TPUs that provides the machine learning research community with
a total of 180 petaflops of raw compute power — at no charge — to support the next wave of breakthroughs.
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A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning | #DataScience #MachineLearning #RT

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

Fast Drawing for Everyone

🎉 Cool @Google creative lab project!

🎨 Fast Drawing for Everyone

  • If you’re interested in learning more about the magic behind AutoDraw, check out “Quick, Draw!”
  • (one of our A.I. Experiments).
  • AutoDraw’s suggestion tool uses the same technology to guess what you’re trying to draw.Big thanks to the artists, designers, illustrators and friends of Google who created original drawings for AutoDraw.HAWRAF, Design StudioErin Butner, DesignerJulia Melograna, IllustratorPei Liew, DesignerSimone Noronha, DesignerTori Hinn, DesignerSelman Design, Creative StudioIf you are interested in submitting your own drawings, you can do that here.
  • We hope that AutoDraw, our latest A.I. Experiment, will make drawing more accessible and fun for everyone.

AutoDraw is a new A.I. Experiment, built by Google Creative Lab, which uses machine learning and artists’ drawings, to help everyone create anything visual, fast.
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Auxiliary [Adventure]

Auxiliary (Artificial Intelligence) - Adventure Map -  - By MatPatCat

  • The story picks up where it was last left off and takes you on a new adventure where you attempt to escape the genius mind of an artificial super intelligence called IRIS.
  • You’ve escaped from IRIS – at least you thought so until very recently.
  • IRIS looks to has captured you again and this time you will be sent through another set of twisted experiments.
  • But this time you remember IRIS and you immediately attempt to escape.

Auxiliary is the long away sequel to Genisys which by the way is another highly recommended map to those of you who haven’t played it already. The story picks up where it was last left off and takes you on a new adventure where you attempt to escape the genius mind of an artificial super intelligence called IRIS.
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”