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A potential disadvantage of gradient-based methods is that they head for the nearest minimum, which is usually not the global minimum. This means that the only difference between these search methods is the speed with which solutions are obtained, and not the nature of those solutions. An important consideration is time complexity, which is the rate at ...

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The method you propose is already known, its basically a numerical approximation to the gradient. It is not used to train neural networks because its well... an approximation. You still need to do two forward passes to get an approximation, which introduces noise and might make the training process fail. Using backpropagation to compute the gradient is an ...

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It is quite common in DQN to instead of having the neural network represent function $f(s,a) = \hat{q}(s,a,\theta)$ directly, it actually represents $f(s)= [\hat{q}(s,1,\theta), \hat{q}(s,2,\theta), \hat{q}(s,3,\theta) . . . \hat{q}(s,N_a,\theta)]$ where $N_a$ is the maximum action, and the input the current state. That is what is going on here. It is ...

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From what I understood in a classifier a common method is that you sample a mini-batch, calculate the loss for every example, calculate the average loss over the whole batch and adjust the weights w.r.t to average loss? (Please correct me if I'm wrong) You are wrong. The weights are adjusted w.r.t. to average gradient, and this must be calculated using ...

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I know that gradient descent allows you to find the local minimum of a function. What I don't know is what exactly that function IS. It's usually called the loss function (and, in general, objective function) and often denoted as $\mathcal{L}$ or $L$ (or something like that, i.e. it is not really important how you denote it). The specific function used as a ...

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Welcome to AI Stack exchange! You're right, as the network is initialised randomly, the resultant function is essentially impossible to get your head around. This is because most of the time the network has >4 dimensions (4 can be graphed with some effort and a lot of color), and as such is literally beyond human comprehension via graphing. So what do we ...

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Backpropagation is actually a lot easier than it is made out to be - if you have a basic understanding of calculus and the chain rule, and the single multi-variable calculus rule that to combine 2 gradient vectors, you simply add them. This is hands down the best walk through of back prop I've found on the internet. If you are still confused after that, ...

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