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automatic differentiation

Automatic Differentiation in Recurrent Neural Networks (RNNs)

In the context of Recurrent Neural Networks (RNNs), automatic differentiation (autodiff) refers to a computational technique that automatically calculates derivatives (gradients) of functions expressed as computer programs—particularly the loss function with respect to the model’s parameters.

Why It Matters in RNNs

RNNs process sequential data by maintaining a hidden state that updates at each time step. To train an RNN, you must compute the gradient of the loss function over all time steps in the sequence using backpropagation through time (BPTT).

This involves:

Manually deriving these gradients is tedious and error-prone, especially for long sequences—this is where autodiff is essential.

How Automatic Differentiation Works

Benefits in RNNs

Example (PyTorch)


	  loss = criterion(output, target)
	  loss.backward()  # Automatic differentiation happens here
	  optimizer.step()
      

Calling loss.backward() triggers automatic differentiation, computing gradients for all parameters, including those reused across the time steps in an RNN.

Summary

Automatic differentiation in RNNs is essential for training. It enables the computation of gradients across time-unrolled sequences automatically and efficiently, supporting gradient-based optimization methods like SGD or Adam without the need for manual derivation.