Artificial neuron
Definition taught in Neural units, part of Statistical learning
Artificial neuron
| Type | Definition |
| Taught in | Neural units |
| Part | Statistical learning |
| Questions using it | 5 |
An artificial neuron, or unit, has inputs x_1,\ldots,x_p, weights w_1,\ldots,w_p, and a bias w_0 (also written \beta or b). The net input, also called the weighted input or the activation, is
\mathrm{net}=\sum_{j=0}^{p} w_j x_j=w_0+w^\top x,\qquad x_0\equiv 1.
The output is o=f(\mathrm{net}) for an activation function f. The bias is the weight on an extra input that is permanently 1, so every later derivative has the same form.
Used in
- Taught in Neural units, where the derivation and examples are.
- Also used in Support vector machines
- Questions that use it (5) — Why an ensemble might improve a tree (answer) · Logic gates · A decision boundary · An XOR network of your own · Why XOR needs an extra layer
- All 7 questions on Neural units
See also
Perceptron · Sigmoid function · Activation function · Perceptron learning rule · A single threshold unit cannot compute XOR · One hidden layer approximates continuous functions · History of the neural unit · Neural unit