In particular, we use this rule to compute both the up and down partial derivatives of the cost function of all the connections feeding into the output layer.
📌 3 Key Takeaways
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Objective & Hypothesis:We present a simplified computational rule for the back-propagation formulas for artificial neural networks.
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Key Findings & Data:Moreover, this rule incorporates both the forward and backward phases of the computations involved in the learning process.
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Conclusion & Impact:In particular, we use this rule to compute both the up and down partial derivatives of the cost function of all the connections feeding into the output layer.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Neural Networks · Deep learning · Back-propagation
Abstract
We present a simplified computational rule for the back-propagation formulas for artificial neural networks. In this work, we provide a generic two-step rule for the back-propagation algorithm in matrix notation. Moreover, this rule incorporates both the forward and backward phases of the computations involved in the learning process. Specifically, this recursive computing rule permits the propagation of the changes to all synaptic weights in the network, layer by layer, efficiently. In particular, we use this rule to compute both the up and down partial derivatives of the cost function of all the connections feeding into the output layer.
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