Function approximation
Function approximation is representing an unknown input-output mapping with a parameterized model fitted from finite observations.
Math and Architectures of Deep Learning
The task of representing a target input-output mapping with a model, used here to justify nonlinear models for complex real-world processes.
- How it is used
- Frames machine learning as approximating unknown classification or estimation functions from training data.
- How deep it goes
- Connects surrogate functions, weights, nonlinear activations, perceptron geometry, and approximation capacity, while deferring practical parameter-count limits.
- Examples
- Uses stock-price estimation, logical gates, XOR, sigmoid curves, and perceptron decision-boundary diagrams.
- What you can do after
- Build it from scratch, formulate weighted models and perceptrons that approximate simple mappings.
Read how Math and Architectures of Deep Learning explains function approximation →
Evolutionary Deep Learning: Genetic algorithms and neural networks
A function approximator is a computational model that estimates the outputs of a target function without necessarily reproducing its exact form.
- How it is used
- Frames machine-learning prediction and evolutionary search as alternative ways to approximate or construct functions.
- How deep it goes
- Defines the role, compares methods, and walks through a runnable Geppy implementation without developing mathematical theory.
- Examples
- Uses continuous polynomials, discontinuous and complex functions, and a Geppy target of 6*x1 + 22.
- What you can do after
- Build it from scratch by configuring Geppy to evolve, evaluate, simplify, and visualize an executable function.
Related concepts
- deep learningaddressed by · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
- machine learningequivalent to · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
- DQNimplemented by · DQN is a reinforcement-learning algorithm that uses a neural network to approximate a state-action value function for selecting actions.
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