The Matrix Calculus You Need for Deep Learning

This web content explains the concept of matrix calculus in the context of training deep neural networks. The authors assume no prior knowledge of math beyond calculus 1 and provide links to refresh the necessary math. They emphasize that understanding matrix calculus is not necessary for practical usage of deep learning but can deepen understanding for those already familiar with neural networks. The paper covers topics such as partial derivatives, gradient vectors, the Jacobian matrix, and element-wise binary operations. It aims to simplify matrix calculus and make it accessible to a wide audience.

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