In bquast/EconometricsUsingR: Package to accompany the Handbook of Statistics: Econometrics Using R

An introductory sentence here.

Backpropagation

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)

Deep Learning

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)

Gradient Descent

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)

Recurrent Neural Networks

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.

bquast/EconometricsUsingR documentation built on May 17, 2019, 8:05 a.m.