Neural Networks as Linear Regression: An Introduction for Statisticians
arXiv:2606.236013.4
Predicted impact top 84% in ML · last 90 daysOriginality Synthesis-oriented
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For statisticians unfamiliar with neural networks, this provides a gentle introduction by linking to familiar linear regression concepts.
This paper aims to lower the barrier for statisticians to understand neural networks by showing how they can approximate linear regression, providing a foundation for further study.
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.