Democratising Fare Storage at Scale Using Event Sourcing
From humble beginnings, Grab has expanded across different markets in the last couple of years. We’ve added a wide range of features to the Grab platform to continue to delight our consumers and…
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Derivation of Back Propagation with Cross Entropy
In order to understand the Back Propagation algorithm, we first need to understand some basic concepts such as Partial Derivatives, chain rule, Cross Entropy loss, Sigmoid function and Softmax function.
Assuming we have already forward-passed the inputs to get some outputs at the last layer Y, we will have to calculate the loss function E and propagate the loss to all the preceding layers by changing the weights associated with each of the layers.
Knowing the cross entropy loss E and the softmax activation “ yi ‘ , we can calculate the change in loss with respect to any weight connecting the output layer using the chain rule of partial derivatives. Intuitively, we can even find the weight gradients for the whole layer using the matrix notation shown below.
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