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In Bayesian statistics, as opposed to frequentist statistics, you can model the parameters as random variables. Bayesian machine learning is the application of Bayesian statistics in the context of machine learning. The specific application of Bayesian statistics to learning in neural networks is denoted as Bayesian deep learning. Even just in the simple ...


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I don't have an exact answer to your question because I think your question is a bit ambiguous. It's like asking "How many defenders do you need to replace your forwards?": they do slightly different things, but a defender occasionally may be able to play as a forward. Anyway, given that I think this question arises because you don't understand how ...


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This is formally known as a semi-gradient method. What we would like to do is to minimize $\big(v(S) - \hat v(S, w)\big)^2$, where $v(S)$ is the true value function. This would give the gradient descent update \begin{align*} w \leftarrow w + \alpha[v(S) - \hat v(S, w)]\nabla \hat v(S, w) . \tag{1} \end{align*} Of course we don't have access to $v(S)$. So ...


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The size of the parameters tensor is depended on what type of layer that you want to build. Convolutional, fully connected, attention or even custom layer, each layer has a difference in the way it treats input, reading the documents is the good way to start (CS231n of Stanford University describes in detail each layer's properties). In your case, the layer ...


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