<ul data-eligibleForWebStory="false"><li>Neural networks excel in capturing complex non-linear patterns in data by learning flexible, layered representations that adapt to the underlying structure.</li><li>Mathematical foundations laid by pioneers like Joseph Fourier, Taylor, and Weierstrass have contributed to understanding non-linearity, forming the basis for modern machine learning algorithms, particularly neural networks.</li><li>Neural networks break complex patterns down by representing them as piecewise continuous or piecewise linear functions, allowing for more manageable approximations in smaller sub-domains.</li><li>The concept of piecewise linearity can be illustrated using Rectified Linear Unit (ReLU) activation functions, showing how neural networks represent complex non-linear patterns through a sum of several ReLU functions.</li></ul>

Neural Nets: Part 1 — Piecewise Linearity

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