| References: [1] Ian Goodfellow., Yoshua Bengio., Aaron Courville. (2016). Deep Learning MIT Press, USA 2016
[2] Bishop, C.M., Neural Network for Pattern Recognition, Clarendon Press, USA 1995
[3] François Chollet. (2018). Deep Learning with Python, Manning Pub., 1st Ed, NY, USA, 2018
[4] Jain, Ajeet K., PVRD Prasad Rao., Venkatesh Sharma, K. (2021). A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning, Computational Analysis and Understanding of Deep Learning or Medical Care: Principles, Methods and Applications. CUDLMC 2020, Wiley-Scrivener, April/May.
[5] Mueller, John Paul., Massaron, Luca. (2019). Deep Learning for Dummies, John Wiley, 2019
[6] Patterson, Josh., Gibson, Adam. (2017). Deep Learning: A Practitioner’s Approach, O’Reilly Pub. Indian Edition, 2017
[7] Jain, Ajeet, K., PVRD Prasad Rao., Sharma, Venkatesh. (2020). Deep Learning with Recursive Neural Network for Temporal Logic Implementation, International Journal of Advanced Trends in Computer Science and Engineering, 9 (4) July – August 2020, 6829-6833.
[8] Srivasatava, et al. http://jmlr.org/papers/volume15/srivastava14a.old/srivastava14a.pdf
[9] Bertsekas, Dimitri P. (2009). Convex Optimization Theory, Athena Scientific Pub., MIT Press, USA.
[10] Boyd, Stephen., Vandenberghe, Lieven. Convex Optimization, Cambridge University Press, USA.
[11] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1 (4) 541–551.
[12] Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580.
[13] Glorot, X., Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), pages 249–256.
[14] Glorot, X., Bordes, A., Bengio, Y. (2011). Deep sparse rectifier neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), pages 315–323.
[15] Zeiler, M. and Fergus, R., Stochastic pooling for regularization of deep convolutional neural networks. In Proceedings of the International Conference on Learning Representations, ICLR, 2013.
[16] Fabian Latorre, Paul Rolland and Volkan Cevher, Lipschitz Constant Estimation Of Neural Networks Via Sparse Polynomial Optimization, ICLR 2020
[17] Kingma, D., Ba, J. (2014). Adam: A method for stochastic optimization, arXiv:1412.6980.
[18] Manzil Zaheer, et al., Adaptive Methods for Nonconvex Optimization, 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. |
