Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/299942 
Autor:innen: 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 17014
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your way up to any other estimator," and apply it to Machine Learning. Focusing on a single-hidden-layer artificial neural network, the paper discusses its mathematical underpinnings, including the pivotal Universal Approximation Theorem—an essential "existence theorem". The exposition extends to the algorithmic exploration of solutions, specifically through "feed forward" and "back-propagation", and rounds up with the practical implementation in Python. The objective of this primer is to equip readers with a solid elementary comprehension of first principles and fire some trailblazers to the forefront of AI and causal machine learning.
Schlagwörter: 
machine learning
deep learning
supervised learning
artificial neural network
perceptron
Python
keras
tensorflow
universal approximation theorem
JEL: 
C01
C87
C00
C60
Dokumentart: 
Working Paper

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