Target function
A target function is an unknown input-output mapping representing the desired behavior that a predictive model aims to approximate.
Math and Architectures of Deep Learning
The unknown function expressing desired input-output behavior that a model aims to approximate.
- How it is used
- Frames intelligent tasks as unknown input-output mappings that neural networks can approximate.
- How deep it goes
- Defines notation, variable types, and neural-network approximation, but stops short of detailing training procedures.
- Examples
- Uses stock-selling decisions with prices and expert advice as inputs and binary sell-or-hold outputs.
- What you can do after
- Understand how real-world decisions become mathematical mappings that neural networks can approximate.
Read how Math and Architectures of Deep Learning explains target function →
Evolutionary Deep Learning: Genetic algorithms and neural networks
A target function is a mathematical function that an evolutionary program is intended to reproduce or approximate.
- How it is used
- Uses a desired mathematical relationship to guide evolutionary search toward an approximating expression.
- How deep it goes
- Defines a linear target, implements its fitness comparison, and exercises changes in complexity without deriving approximation theory.
- Examples
- Uses f(x1) = 6x1 + 22, sample-input predictions, mean absolute error, and a simplified evolved equation.
- What you can do after
- Build it from scratch with Geppy by defining a target, evolving candidate expressions, measuring error, and inspecting the best result.
Related concepts
- language modelimplemented by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- functionequivalent to · A function is a callable program unit that performs an operation, may accept inputs, and can produce a result or side effect.
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