___

• Niyazi TELÇEKEN & Murat KIYILAR & Eyüp KADIOĞLU, “Volatilite Endeksleri: Gelişimi, Türleri, Uygulamaları ve Trvıx Önerisi”, Ekonomi, Politika & Finans Araştırmaları Dergisi, 2019, 4(2): 204-228
• Özkan ŞAHİN & Mehmet Akif ÖNCÜ “Volatilite Alanında Yapılmış Lisansüstü Tezelere Yönelik Bir İçerik Analizi” Muhasebe ve Finansman Dergisi
• Breiman L., Cutler A., Random forest, http://www.stat. berkeley.edu/~breiman/RandomForests/cc_home.html, (2005)
• Alexey Natekin, Alois Knoll, Gradient boosting machines, a tutorial, Neurorobot., 04 December 2013
• Silva F M and Almeida L B 1990, Acceleration techniques for the backpropagation algorithm Neural Networks ed L B Almeida andCJWellekens (Berlin: Springer)
• Ayşe ARI,Murat Erşen BERBERLER, Yapay Sinir Ağları ile Tahmin ve Sınıflandırma Problemlerinin Çözümü İçin Arayüz Tasarımı, Acta Infologıca – 2017
• Y. LeCun et al., "Backpropagation applied to handwritten zip code recognition," Neural computation, vol. 1, no. 4, pp. 541-551, 1989.
• Stephane Lathuili ´ ere, Pablo Mesejo, Xavier Alameda-Pineda, ` Member IEEE, and Radu Horaud “A Comprehensive Analysis of Deep Regression “ https://arxiv.org/abs/1803.08450, v3 [cs.CV] 24 Sep 2020
• Antoni Wysocki and Maciej Ławryńczuk, Two- and Three-Layer Recurrent Elman Neural Networks as Models of Dynamic Processes, February 2016, Challenges in Automation, Robotics and Measurement Techniques (pp.165-175)
• Ercan Öztemel, Yapay Sinir Aglari Paperback – January 1, 2003
• Vidushi Sharma, Sachin Rai, Anurag DevA Comprehensive Study of Artificial Neural Networks, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 10, October 2012, ISSN: 2277 128X
• I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (Adaptive computation and machine learning). Cambridge, Massachusetts: The MIT Press, pp. xxii, 775 pages, 2016.
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
• V. Dumoulin and F. Visin, "A guide to convolution arithmetic for deep learning," CoRR, vol. abs/1603.07285, 2016.
• Evrişimli Sinir Ağları el kitabı, CS 230 - Derin Öğrenme https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-convolutional-neural-networks
• Ren, G., Cao, Y., Wen, S., Huang, T., & Zeng, Z. (2018). A modified Elman neural network with a new learning rate scheme. Neurocomputing, 286, 11–18. doi: 10.1016/j.neucom.2018.01.046
• Wysocki, A., & Ławryńczuk, M. (2016). Two- and Three-Layer Recurrent Elman Neural Networks as Models of Dynamic Processes. Advances in Intelligent Systems and Computing, 165–175. doi:10.1007/978-3-319-29357-8_15
• Shuaiqiang Liu, Cornelis W. Oosterlee and Sander M. Bohte Pricing Options and Computing Implied Volatilities using Neural Networks, Modern Numerical Techniques and Machine-Learning in Pricing and Risk Management, https://doi.org/10.3390/risks7010016, 2019
• Chuong Luong and Nikolai Dokuchaev, Forecasting of Realised Volatility with the Random Forests Algorithm, Journal of Risk and Financial Management, 2018
• Winky K.O. Ho, Bo-Sin Tang Predicting property prices with machine learning algorithms, Journal of Property Research, 2020
• Wenjie Lu, Jiazheng Li, Jingyang Wang, Lele Qin, A CNN-BiLSTM-AM method for stock price prediction, Neural Computing and Applications, 2020
• Bo Liu , Qilin Wu, and Qian Cao, An Improved Elman Network for Stock Price Prediction Service, Volume 2020, Article ID 8824430, 9 pages
• Fang Wang, Sai Tang and Menggang Li, Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market, Volume 2021 |Article ID 6641298
• Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai ¸Gradient Descent Finds Global Minima of Deep Neural Networks, arXiv:1811.03804, 2019
• Yuanzhi Li, Yingyu Liang, Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data, arXiv:1808.01204, 2018