Advanced · Neural Networks
Backpropagation through structure (BPTS)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A training technique for recurrent neural networks that calculates gradients by backpropagating errors through the network's structure.
Technical Definition
A training technique for recurrent neural networks that calculates gradients by backpropagating errors through the network's structure.
How it works
Backpropagation Through Structure (BPTS) is an extension of backpropagation designed for training recurrent neural networks (RNNs). It enables the calculation of gradients by propagating errors backward through the recurrent connections and the network's overall structure.
Related Concepts
- Gradient Descent — An optimization algorithm that iteratively adjusts model parameters by moving in the direction of steepest decrease of the loss function.
- Recurrent Neural Network (RNN) — A neural network with loops that maintain hidden state, designed to process sequential data like text and time series.
- Backpropagation through time (BPTT) — A core algorithm for training recurrent neural networks by unfolding them over time and applying backpropagation.