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Alright. Consider an ordinary neural network, so, in the last layer, we have, $z^{[L]} = W^{[L]} a^{[L-1]} + b^{[L]}$, where $a^{[L]} = \sigma(z^{[L]})$, where $\sigma$ is the softmax activation: $$ \sigma(\mathbf z)_{i} = \frac{e^{z_i}}{\sum_k e^{z_k}} $$ I think, one of the most effective ways of not to get confused about all these matrices with different ...


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This is how I see it: The state that a purely reactive agent is reacting to is, in fact, a subset of the set of all possible runs that end with a state. So in theory, E (some state) is a subset of R (finite set of all possible runs with a state as the last element). Standard and purely reactive agents are similar in the sense that the agents' purpose is to ...


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$\nabla_{\theta_{k-1}} \theta_k$ is gradient of $\theta_k$ with respect to $\theta_{k-1}$, it follows chain rule as noted in the side comment in the image. $\nabla_{\theta} \mathcal L(\theta_k)$ is also not a Hessian but a gradient vector.


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Every model is a function. Not every function is a model. A function uniquely maps elements of some set to elements of another set, possibly the same set. Every AI model is a function because they are implemented as computer programs and every computer program is a function uniquely mapping the combination of the sequence of bits in memory and storage at ...


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Any model can be considered to be a function. The term "model" simply denotes a function being used in a particular way, namely to approximate some other function of interest.


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In simple terms, a neural network model is a function approximator which tries to fit the curve of the hypothesis function. A function itself has an equation which will generate a fixed curve: If we have the equation (i.e., the function), we do not need neural network for its input data. However, when we only have some notion of its curve (or the input and ...


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Although this may not be applicable to all cases, I like to think of a model as a set of functions, so here's the difference. Why is this definition useful? If you think of a neural network with a vector of parameters $\theta \in \mathbb{R}^m$ as a model, then a specific combination of these parameters represents a specific function. For example, suppose ...


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