Intermediate · Neural Networks
Backpropagation through time (BPTT)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A core algorithm for training recurrent neural networks by unfolding them over time and applying backpropagation.
Technical Definition
A core algorithm for training recurrent neural networks by unfolding them over time and applying backpropagation.
How it works
Backpropagation Through Time (BPTT) is a fundamental algorithm used to train recurrent neural networks (RNNs). It works by 'unrolling' the network into a deep feedforward structure, where each time step is represented, and then applying the standard backpropagation algorithm to compute gradients.
Related Concepts
- Gradient Descent — An optimization algorithm that iteratively adjusts model parameters by moving in the direction of steepest decrease of the loss function.
- Backpropagation — An algorithm that efficiently computes gradients by propagating errors backward through the network using the chain rule.
- Recurrent Neural Network (RNN) — A neural network with loops that maintain hidden state, designed to process sequential data like text and time series.