New answers tagged backpropagation
2
Optimizing the cross-entropy is equivalent to optimizing the log-likelihood of the parameters given the data, $\ell(\theta)$, which is what we want, i.e. find the parameters that most likely generated the data.
So, the likelihood is defined as $$\mathcal{L}(\theta) = P(y \mid x; \theta),$$
i.e. a function of the parameters $\theta$.
The log-likelihood is ...
1
There are known architectures to implement this idea, namely, seamese networks, and also a training strategy, known as contrastive learning, that relies on the idea of comparing the output of neural networks. I will explain both of them briefly.
The idea of seamese networks is exactly what you mentioned. You have a single model, $m$, that receives two inputs,...
Top 50 recent answers are included