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A loss function is a function L:(z,y)∈R×Y⟼L(z,y)∈R that takes as inputs the predicted value z corresponding to the real data value y and outputs how different they are.
The common loss functions are summed up in the table below:
Least squared error | Logistic loss | Hinge loss | Cross-entropy |
1/2(y−z)2 | log(1+exp(−yz)) | max(0,1−yz) | −[ylog(z)+(1−y)log(1−z)] |
Linear regression | Logistic regression | SVM | Neural Network |
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torch.matmul(a, b) # multiples torch tensors a and b * # element-wise multiplication between two torch tensorstorch.eye(n) # creates an identity torch tensor with shape (n, n) torch.zeros(n, m) # creates a torch tensor of zeros with shape (n, m) torc...
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