From a blog post from Andrej Karpathy on training neural networks:
Initialize the final layer weights correctly. E.g. if you are regressing some values that have a mean of 50 then initialize the final bias to 50. If you have an imbalanced dataset of a ratio 1:10 of positives:negatives, set the bias on your logits such that your network predicts probability of 0.1 at initialization.
I don't understand the bolded sentence at all. What does predict 0.1 mean? Is it that all the outputs should be 0.1? How would one go about doing that?