An introductory sentence here.
The chapter "Neural Networks" begins with generating data for the backpropagation example.
backpropagation_data(n=50000)
We can now very the objects in our Global Environment.
ls()
And then run the training code.
backpropagation_training(X, Y)
Next we move to deep learning, first by generating the data.
deeplearning_data(n=50000)
We can now very the objects in our Global Environment.
ls()
And then run the training code.
deeplearning_training(X, Y)
Here gradient decent comes in, first we generate data.
gradientdescent_data(n=50000)
We can now very the objects in our Global Environment.
ls()
And then run the training code.
gradientdescent_training(X, Y, hidden_dim=4, alpha=0.1)
Finally the examples for recurrent neural networks.
recurrentneuralnetwork_data(n=10000, binary_dim=8)
Check the data.
ls()
Now run the training.
recurrentneuralnetwork_training(X1, X2, Y, hidden_dim=10, alpha=0.1)
Return to the overview page.
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