Backpropagation in matrix notation
N. M. Mishachev
TL;DR
This paper derives the gradient of neural network functions using matrix notation, simplifying the understanding and computation of backpropagation.
Contribution
It introduces a matrix notation approach to compute gradients in neural networks, providing a clearer and potentially more efficient method.
Findings
Matrix notation simplifies backpropagation calculations.
Provides a unified framework for gradient computation.
Potentially improves computational efficiency.
Abstract
In this note we calculate the gradient of the network function in matrix notation.
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Taxonomy
TopicsComplex Network Analysis Techniques · Topological and Geometric Data Analysis · Graph theory and applications