Oh God, the horror. Almost every time neural network library I've seen manages to obscure a very simple idea in piles of useless objects.
"Neural networks" are just the composition of several nonlinear regressions. There's nothing particularly "neural" about them.
Here's a typical 3-layer network:
f(x,Wh,Wo) = tanh(Wo * tanh(Wh * X))
Wh, and Wo are the hidden and output weight matrices respectively. Fix some loss function (ie, L(x,Wh,Wo,y) = || f(x,Wh,Wo) - y||^2), get the gradient of this function, and a take step down the gradient. There's your learning rule.
Now, I understand the desire for flexibility/modularity, but (1) what's the sense of trying to house supervised and unsupervised methods in the same hierarchy?
and
(2) what could possibly justify Connection and Weight objects?
"Neural networks" are just the composition of several nonlinear regressions. There's nothing particularly "neural" about them.
Here's a typical 3-layer network:
f(x,Wh,Wo) = tanh(Wo * tanh(Wh * X))
Wh, and Wo are the hidden and output weight matrices respectively. Fix some loss function (ie, L(x,Wh,Wo,y) = || f(x,Wh,Wo) - y||^2), get the gradient of this function, and a take step down the gradient. There's your learning rule.
Now, I understand the desire for flexibility/modularity, but (1) what's the sense of trying to house supervised and unsupervised methods in the same hierarchy? and (2) what could possibly justify Connection and Weight objects?