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Some research areas that come to mind which can be useful when faced with a limited amount of data: Regularization: Comprises different methods to prevent the network from overfitting, to make it perform better on the validation data but not necessarily on the training data. In general, the less training data you have, the stronger you want to regularize. ...


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At a basic level, these kinds of low-dimensional plots where you look at one or two variables at a time can help to give you a sense of what types of relationships you might expect to see, such as linear, non-linear, or periodic relationships, which can steer you toward an appropriate family of models. You wouldn't want to use a linear model to predict data ...


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I no longer really use validation that much, but rather only training and testing. Why? Because I mostly follow Ron Kohavi's (Stanford Univ) approach to CV. I have done a lot of validation but it seemed to be overkill, essentially causing me to ask why I have this very small-sampled parameter watch on the side from which I am supposed to learn from. You ...


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My understanding is that AI can be understood as a very generalized and abstract statistics software package handling input data in a general way to find the "best fit" to some form of problem. Is that correct? I know it isn't. But is it vaguely correct? No. It's not correct, in my opinion, not even vaguely and in many ways. AI is not (...


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