Year: 2026
Type: preprint
Abstract: Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remains high, particularly for classical statisticians trained in a frequentist perspective. In this letter, we demystify neural networks by describing networks that approximate a linear regression and describe common customizations that provide a foundation for further study.
Source: arXiv (Cornell University)
Authors: Loe, Abigail, Murray, Susan, Wu, Zhenke
Institutions: Macalester College, University of Michigan, Michigan United
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165680815
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