Basics of Deep Learning
作者： Uday Kamath , John Liu , James Whitaker , Uday Kamath , John Liu
DOI: 10.1007/978-3-030-14596-5_4
关键词: Academic community 、 Field (Bourdieu) 、 Artificial neural network 、 Computer science 、 Cognitive science 、 Artificial intelligence 、 Representation (systemics) 、 Deep learning
摘要: One of the most talked-about concepts in machine learning both academic community and media is evolving field deep learning. The idea neural networks, subsequently learning, gathers its inspiration from biological representation human brain (or any brained creature for that matter).
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Matthew D. Zeiler, ADADELTA: An Adaptive Learning Rate Method arXiv: Learning. ,(2012)
Yoshua Bengio, Frederic Morin, Hierarchical Probabilistic Neural Network Language Model. international conference on artificial intelligence and statistics. ,(2005)
Christopher M. Bishop, Regularization and complexity control in feed-forward networks EC2 et Cie. ,vol. 1, pp. 141- 148 ,(1995)
B. Speelpenning, Compiling fast partial derivatives of functions given by algorithms University of Illinois at Urbana-Champaign. ,(1980) , 10.2172/5254402
Andreas Griewank, Andrea Walther, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation ,(1987)
Gérard Ben Arous, Anna Choromanska, Yann LeCun, Mikael Henaff, Michael Mathieu, The Loss Surfaces of Multilayer Networks international conference on artificial intelligence and statistics. ,vol. 38, pp. 192- 204 ,(2015)
Katta G. Murty, Santosh N. Kabadi, Some NP-complete problems in quadratic and nonlinear programming Mathematical Programming. ,vol. 39, pp. 117- 129 ,(1987) , 10.1007/BF02592948
Ilya Sutskever, Geoffrey Hinton, Alex Krizhevsky, Ruslan Salakhutdinov, Nitish Srivastava, Dropout: a simple way to prevent neural networks from overfitting Journal of Machine Learning Research. ,vol. 15, pp. 1929- 1958 ,(2014)
, Generative Adversarial Nets neural information processing systems. ,vol. 27, pp. 2672- 2680 ,(2014) , 10.3156/JSOFT.29.5_177_2
Geoffrey E Hinton, Ruslan R Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks Science. ,vol. 313, pp. 504- 507 ,(2006) , 10.1126/SCIENCE.1127647