Neural Network Theory Lecture Note 1 - Approximation Theorem
Pre-requisites:
- It will be helpful to know some basic concept about functional analysis, e.g. linear functional, normed space, Hahn-Banach Theorem; anyway they will be explained in my note
- Basic form of single-layer neural network.
After the happy summer vacation, I have to attend the master program at ETH Zurich. This is my lecture note for my first course, Neural Network Theory, at ETH.
Lecturer: Prof. Helmut Elbrächter
This first note is about the capacity of a single-layer neural network in approximating an arbitrary function whose domain is in ${\rm \pmb{R}}^d$.
To view this note, please download:
NNT_note1_tongyulu.pdf
本文作者： lucainiaoge
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