Study The Stages of Development of Artificial Intelligence
Keywords:
Artificial neuron, artificial intelligence, machine learning, artificial superintelligence, intellectual activity.Abstract
This article provides information on the general classification and structure of artificial neural networks and the tasks they solve. The areas where artificial neural networks are used and areas of their application were also discussed. At the same time, in today's advanced technology era, artificial intelligence and neural network technologies occupy an important place in the life of society. Artificial neural networks have been studied as solutions to complex problems where traditional algorithmic solutions are inefficient or impossible.
References
Jordan, J. Intro to optimization in deep learning: Gradient Descent/ J. Jordan // Paper-space. Series: Optimization. – 2018. – URL: https://blog.paperspace.com/intro-to-optimiza-tion-indeep-learning-gradient-descent/
Scikit-learn – машинное обучение на Python. – URL: http://scikit-learn.org/stable/ modules/generated/sklearn.neural_network. MLPClassifier.html
Keras documentation: optimizers. – URL: https://keras.io/optimizers
Ruder, S. An overview of gradient descent optimization algorithms / S. Ruder // Cornell University Library. – 2016. – URL: https://arxiv. org/abs/1609.04747
Robbins, H. A stochastic approximation method / H. Robbins, S. Monro // The annals of mathematical statistics. – 1951. – Vol. 22. – P. 400–407.
Kukar, M. Cost-Sensitive Learning with Neural Networks / M. Kukar, I. Kononenko // Machine Learning and Data Mining : proceed-ings of the 13th European Conference on Artificial Intelligence. – 1998. – P. 445–449.
Duchi, J. Adaptive Subgradient Methods for Online Learning and Stochastic Optimiza-tion / J. Duchi, E. Hazan, Y. Singer // The Jour-nal of Machine Learning Research. – 2011. – Vol. 12. – P. 2121–2159. 8. Zeiler, M. D. ADADELTA: An Adap-tive Learning Rate Method / Cornell Univer-sity Library. – 2012. – URL: https://arxiv.org/ abs/1212.5701
Kingma, D. P. Adam: A Method for Sto-chastic Optimization / D. P. Kingma, J. Ba // Cornell University Library. – 2014. – URL: https:// arxiv.org/abs/1412.6980
Гудфеллоу, Я. Глубокое обучение / Я. Гу-дфеллоу, И. Бенджио, А. Курвилль. – М. : ДМК Пресс, 2018. – 652 с.
Fletcher, R. Practical methods of optimi-zation / R. Fletcher. – Wiley, 2000. – 450 p.
Schraudolph, N. N. A Stochastic Qua-si-Newton Method for Online Convex Optimiza-tion / N.N. Schraudolph, J. Yu, S. Gunter // Sta-tistical Machine Learning. – 2017. – URL: http:// proceedings.mlr.press/v2/schraudolph07a/ schraudolph07a.pdf
Ruder, S. Optimization for Deep Learn-ing Highlights in 2017 / S. Ruder // Optimization for Deep Learning Highlights in 2017. – 2017. – URL: http://ruder.io/deep-learning-optimization-2017.