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Oracle Drags Its Feet in the JavaScript Trademark Dispute
Oracle seeks to dismiss fraud claims in the JavaScript trademark dispute, delaying the case and avoiding questions about its right to the name.
A Neural Network written in Ruby with an objected oriented implementation. Training is done using http://en.wikipedia.org/wiki/Backpropagation and http://en.wikipedia.org/wiki/Gradient_descent.
The simple API was inspired from https://github.com/harthur/brain
network = Noggin::Network.new
network.train([
{ input: [0, 0], expected: 0 },
{ input: [0, 1], expected: 1 },
{ input: [1, 0], expected: 1 },
{ input: [1, 1], expected: 0 }
])
network.run [0, 0] # 0.0163
network.run [0, 1] # 0.9873
network.run [1, 0] # 0.9702
network.run [1, 1] # 0.0142
gem install the_noggin
Noggin::Network.new(
training_laps: 100000, # How many propgation of errors to do when training
learning_rate: 0.1, # How fast the network learns
momentum: 0.2, # How much of previous weight deltas should be applied to next delta
hidden_layer_size: 1 , # Number of hidden layers
hidden_layer_node_size: 2, # Number of nodes each hidden layer has
)
FAQs
Unknown package
We found that the_noggin demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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