Computation Graph
Computation Graph is a directed acyclic graph that represents a mathematical computation. It is a visual representation of a mathematical model that allows efficient computation of gradients for optimization algorithms. Computation graphs are used in deep learning to represent the forward and backward pass of a neural network. The nodes in the graph represent mathematical operations, while the edges represent the flow of data between the operations. Computation graphs are used to compute the gradients of the loss function with respect to the parameters of the model, which are then used to update the parameters using optimization algorithms such as stochastic gradient descent.
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