matrix derivatives
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Simplifying Backpropagation with Intuitive Derivatives
Read Full Article: Simplifying Backpropagation with Intuitive Derivatives
Understanding backpropagation in neural networks can be challenging, especially when focusing on the dimensions of matrices during matrix multiplication. A more intuitive approach involves connecting scalar derivatives with matrix derivatives, simplifying the process by saving the order of expressions used in the chain rule and transposing matrices. For instance, in the expression C = A@B, the derivative with respect to A is expressed as @B^T, and with respect to B as A^T@, which simplifies the understanding of derivatives without the need to focus on dimensions. This method offers a more insightful and less mechanical way to grasp backpropagation, making it accessible for those working with neural networks.
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