Assertion: neural network's function | /r/CapableOf | map input
Facets:
value: to the output
type: TRANSITIVE-OBJECT
value: to output
type: PURPOSE
value: based on some learnt mapping
type: OTHER
value: from data
type: OTHER
Source sentences:
text: Now we need a neural network to learn and represent almost anything and any arbitrary complex function that maps an input to output.
text: In the modern sense, the perceptron is an algorithm for learning a binary classifier called a threshold function: a function that maps its input x {\displaystyle \mathbf {x} } (a real-valued vector) to an output value
text: Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features.
text: If you were using a very strict approach towards defining a supervised machine learning model, you would for example say that machine learning produces a function which maps some input to some output based on some learnt mapping, which equals the mapping made by the true, underlying mapping in your data.
text: Now we need a neural network to learn and represent almost anything and any arbitrary complex function that maps an input to output.
text: Supervised learning refers to a task where we need to find a function that can map input to corresponding outputs (given a set of input-output pairs).