Neural networks have the ability to perform a wide array of impressive tasks. Individuals without a robust theoretical background can utilize libraries to create neural networks for various purposes. However, a grasp of the theoretical foundations becomes crucial for those aiming to develop more efficient neural networks. The theoretical underpinnings of neural networks largely involve applied mathematics. While the mathematics can sometimes be intricate, the fundamental concepts are not as complex as they may initially appear.
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