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Results tagged with gradient-descent
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user 227027
Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. For stochastic gradient descent there is also the [sgd] tag.
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How do hidden layer of a trained network look like?
Suppose I have a deep feed-forward neural network with sigmoid activation $\sigma$ already trained on a dataset $S$. Let's consider a training point $x_i \in S$. I want to analyze the entries of a hid …
- 345
2
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Learning Manifolds using Gradient Descent
I have a feedforward neural network $F(W): \mathbb R^d \rightarrow \mathbb R^k$ with $Relu$ activation, $m$ neurones per layer, $L$ layers and softmax on the output layer. $W$ denotes the weight matri …
- 345