Jul 10, 2017 · Bottom line: In layman's terms, one could think of cross-entropy as the distance between two probability distributions in terms of the amount of information (bits) needed to …

Jul 19, 2018 · 128 You will need some conditions to claim the equivalence between minimizing cross entropy and minimizing KL divergence. I will put your question under the context of …

Jan 25, 2017 · During the training of a simple neural network binary classifier I get an high loss value, using cross-entropy. Despite this, accuracy's value on validation set holds quite good.

Nov 20, 2018 · Cross-entropy with one-hot encoding implies that the target vector is all $0$, except for one $1$. So all of the zero entries are ignored and only the entry with $1$ is used …

However, the MSE loss captures this change by increasing too. So my question is why do we need cross-entropy loss? MSE loss seems to work fine. Or is it to do with the fact that the …

Apr 2, 2020 · Somewhere along the way, cross-entropy became the dominant loss function that is used in many papers and almost all the "blog" type references on NN. Recall that the cross …

Mar 7, 2018 · One simple one is the beta distribution. In that case, our prediction would be the 2 parameters $\alpha$ and $\beta$. Seem complicated? Fortunately, a continuous version of …

Sep 18, 2016 · Here is one of the cleanest and well written notes that I came across the web which explains about "calculation of derivatives in backpropagation algorithm with cross …

Jul 15, 2020 · I usually see a discussion of the following loss functions in the context of the following types of problems: Cross entropy loss (KL divergence) for classification problems …

What does the sparse refer to in sparse categorical cross-entropy? I thought it was because the data was sparsely distributed among the classes.