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 Maddede Bu Makale
🎯
Amaç & Hipotez:We present a simplified computational rule for the back-propagation formulas for artificial neural networks.
📊
Temel Bulgular & Veri:Moreover, this rule incorporates both the forward and backward phases of the computations involved in the learning process.
💡
Sonuç & Katkı: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.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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.
Boughammoura, A. (2023). A Two-Step Rule for Backpropagation. International Journal of Informatics and Applied Mathematics, Volume: 6 Issue: 1. https://doi.org/10.53508/ijiam.1265832
Alber, M., Bello, I., Zoph, B., Kindermans, P. J., Ramachandran, P., & Le, Q. (2018). Backprop evolution. Preprint at https://arxiv.org/abs/1808.02822.
Baldi, P. (2021). Deep learning in science. Cambridge University Press.
Hojabr, R., Givaki, K., Pourahmadi, K., Nooralinejad, P., Khonsari, A., Rahmati, D., & Najafi, M. H. (2020, October). TaxoNN: A Light-Weight Accelerator for Deep Neural Network Training. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323 (6088), 533-536.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Ye, J. C. (2022). Geometry of Deep Learning. Springer Singapore.