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| | dm13450.github.io
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| | In this post I will be making use of these R packages. Download them all from CRAN to follow along.
| | juliasilge.com
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| | A data science blog
| | www.tylermw.com
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| | neuralnetworksanddeeplearning.com
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| [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens...