LECTURE #9: FUZZY LOGIC & NEURAL NETS
The year 1650 is re-scaled as 0.0 and 2025 as 1.0 and we interpolate linearly in between for all the other years. The reason for doing such a normalization is that it is customary (and often required) for neural networks to scale the data between zero and unity. Since our largest considered year will be 2025 it will be re-scaled as unity. ................
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