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Neural Networks as Function Approximators | Universal Approximation Theorem #neuralnetworks #ai #ml

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Function approximation is a method for estimating an unknown function using observations from the domain data. Artificial neural networks can learn to approximate a function. In supervised learning, a dataset is comprised of inputs and outputs, and the supervised learning algorithm learns how to best map examples of inputs to examples of ...

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AI • Machine Learning • Tech on Instagram: "Neural networks are used to approximate functions when we have inputs and want to generate specific outputs without knowing the exact function (we need an approximation). We do this by training the network on examples, providing it with pairs of inputs and expected outputs. Once trained, the network can generalize and make predictions on new inputs by applying what it learned, essentially approximating the underlying function. C: emergent garden (yt) J

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