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Backpropagation in Neural Networks Explained by Nandita Bhanja Chaudhuri is a document available to read on EtoBox.
The document discusses the necessity and complexity of backpropagation in neural networks, emphasizing the computation of partial derivatives in computational graphs. It explains the challenges of differentiating complex composition functions and introduces dynamic programming as a solution to reduce the computational complexity from exponential to polynomial time. The text also covers various approaches to compute derivatives, including pre-activation and post-activation variables, and addresses shared wei
- Author
- Nandita Bhanja Chaudhuri
- Language
- EN