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Let's suppose that we have an MLP with $15$ inputs, $20$ hidden neurons and $2$ output neurons. The operations performed are only in the hidden and output neurons, given that the input neurons only represent the inputs (so they do not perform any operation). Each hidden neuron performs a linear combination of its inputs followed by the application of a non-...


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I don't think there's a "standard way" of expressing the forward pass: you use the transpose when you need to use it, and this depends on how you define the weights and inputs matrices, and on the architecture of your neural network. For example, in a fully connected feedforward neural network, you know that every neuron in the previous layer is ...


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