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All modern frameworks for deep learning (PyTorch, Jax, Tensorflow) support automatic differentation. These operations can be easily implemented. Here I write, how it would look like in PyTorch: class Net(nn.Module): def __init__(self): super().__init__() self.a = nn.Parameter(torch.randn(1)) self.b = nn.Parameter(torch.randn(...

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Without the specific context, I cannot give a definitive answer, but it's very likely that a "differentiable architecture" refers to a neural network that represents/computes a differentiable function (so you need to use differentiable activation functions, such as the sigmoid), i.e. you can take the partial derivatives of the loss function with ...

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We need to compute the gradients in-order to train the deep neural networks. Deep neural network consists of many layers. Weight parameters are present between the layers. Since we need to compute the gradients of loss function for each weight, we use an algorithm called backprop. It is an abbreviation for backpropagation, which is also called as error ...

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