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Backpropagation Through Time (BPTT)
In an RNN, backpropagation needs to take into account that the model is carrying forward information from each neural layer to the next, and fine tune the weights that govern this “short-term memory”.
This is called Backpropagation Through Time (BPTT). BPTT uses the chain rule to go back from the latest time step to the previous step, and then to the next-previous, each time using gradient descent to discover the best weights for each neuron and for the hidden state function.
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Backpropagation Through Time (BPTT)
A model designed to process sequential data is evaluated on a sequence of 4 time steps. The loss (error) is calculated independently at each time step, yielding the following values: [0.2, 0.5, 0.1, 0.4]. Based on the standard method for computing the total loss for the entire sequence, what is the final loss value?
Evaluating Loss Calculation Strategies
Rationale for Averaging Time-Step Losses
Backpropagation Through Time (BPTT)
Back-Propagating through Discrete Stochastic Operations
Neural Network Learning Rate
Back-Propagation through Random Operations
Backward Propagation Formulation
True/False: During forward propagation, in the forward function for a layer ll you need to know what is the activation function in a layer (Sigmoid, tanh, ReLU, etc.). During back propagation, the corresponding backward function also needs to know what is the activation function for layer ll, since the gradient depends on it.
Back Propagation Illustrated Example
A neural network is trained to distinguish between images of 'apples' and 'oranges'. During a training iteration, it is shown an image of an apple but predicts 'orange' with a high degree of certainty. This results in a significant error value. What is the primary computational goal of the backpropagation step that immediately follows this prediction?
Token-Level Loss Calculation in a Backward Pass
Consider a simple neural network with one input neuron, one hidden neuron, and one output neuron. The network has a weight
w1connecting the input to the hidden neuron, and a weightw2connecting the hidden neuron to the output neuron. After a forward pass, an error is calculated based on the network's final output. To updatew1using the backpropagation algorithm, you must calculate the partial derivative of the error with respect tow1. Which of the following components is essential for determining how much of the final error is attributable to the hidden neuron's activity?Allocating Gradient Memory
Chain Rule for Tensors
Storage of Intermediate Variables in Backpropagation