<ul><li>The Universal Approximation Theorem states that a neural network with a single hidden layer and a nonlinear activation function can approximate any continuous function.</li><li>Different neural network architectures are developed for various tasks, such as using transformers for natural language processing and convolutional networks for image classification.</li><li>Neural network architectures are inspired by the structure in the data, particularly from a physics perspective that involves symmetry and invariance.</li><li>Convolutional neural networks work well with images by preserving local information through kernels with learnable parameters, reducing the need to flatten all pixels and saving memory and computational resources.</li></ul>

Why Are Convolutional Neural Networks Great For Images?

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