The activation function is used to bring the output within an expected range. This is usually a kind of proportional compression function. The sigmoid function is common.. What an activation ...

Machine learning ain't all input/output There's a paper in the journal PLoS Computational Biology that is incredibly significant to folks thinking through the intersection of human-computer ...

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically ...

The demo begins by setting up a 2-15-3 RBF network. There are two input nodes, 15 hidden nodes, and three output nodes. You can imagine that the RBF network corresponds to a problem where the goal is ...

The defining characteristic of deep learning is that the model being trained has more than one hidden layer between the input and the output. In most discussions, deep learning means using deep ...

Machine learning can be supervised, unsupervised, or semi-supervised. In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output.