can be any non-linear differentiable function like sigmoid, tanh, ReLU, etc. (commonly used in the deep learning community). Learning in neural networks is nothing but finding the optimum weight ...

Deep learning is a form of machine ... of neurons “dying” during training if the learning rate is set too high. The output of the activation function can pass to an output function for ...

In a study published in Nature Communications, researchers at the University of Wisconsin–Madison introduced a deep learning ...

Comparison of whole slide image–based deep learning algorithms and genomic classifiers for assessing the risk of prostate cancer metastasis in surgically treated patients. Long-term results of dose ...