Neural Networks are Function Approximation Algorithms

Last Updated on August 27, 2020

Supervised learning in machine learning can be described in terms of function approximation.

Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning algorithms to approximate this function.

Neural networks are an example of a supervised machine learning algorithm that is perhaps best understood in the context of function approximation. This can be demonstrated with examples of neural networks approximating simple one-dimensional functions that aid in developing the intuition for what is being learned by the model.

In this tutorial, you will discover the intuition behind neural networks as function approximation algorithms.

After completing this tutorial, you will know:

• Training a neural network on data approximates the unknown underlying mapping function from inputs to outputs.
• One dimensional input and output datasets provide a useful basis for developing the intuitions for function approximation.
• How to develop and evaluate a small neural network for function approximation.

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