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adl1995.github.io | ||
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michael-lewis.com
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| | | | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | |
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www.lesswrong.com
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| | | | | A neural net using rectified linear unit activation functions of any size is unable to approximate the function sin(x) outside a compact interval. ... | |
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www.v7labs.com
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| | | | | A neural network activation function is a function that is applied to the output of a neuron. Learn about different types of activation functions and how they work. | |
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datadan.io
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| | | Linear regression and gradient descent are techniques that form the basis of many other, more complicated, ML/AI techniques (e.g., deep learning models). They are, thus, building blocks that all ML/AI engineers need to understand. | ||
