ML from Scratch, Part 3: Backpropagation (2019)
The article explains backpropagation in neural networks, detailing equations, matrix operations, and activation functions. It emphasizes linear algebra and calculus, model fitting, parameter optimization, and binary cross-entropy for minimizing loss. Calculating gradients and deltas iteratively is crucial.
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This article discusses the implementation of the backpropagation algorithm in neural networks. It explains the key equations involved in a fully-connected feed-forward neural network, detailing the matrix operations and activation functions used in each layer. The article emphasizes the importance of linear algebra and calculus in understanding backpropagation. It also touches on the implications of the algorithm in terms of model fitting and parameter optimization, particularly focusing on minimizing loss using binary cross-entropy. The mathematical derivations for calculating gradients with respect to weights and biases in each layer are outlined, showcasing the iterative nature of backpropagation. The article concludes by introducing a recursive relation for calculating deltas in each layer, highlighting the iterative implementation of the backpropagation algorithm.
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