Leading business in the age of AI, https://news.microsoft.com/europe/features/leaders-look-to-embrace-ai-and-high-growth-companies-are-seeing-the-benefits, [29.11.2020].
Przewodnik po strukturze ML.NET, https://docs.microsoft.com/pl-pl/dotnet/machine-learning/how-does-mldotnet-work, [30.11.2020].
M. N. Gevorkyan, A. V. Demidova, T. S. Demidova, A. Sobolev, Review and comparative analysis of machine learning libraries for machine learning, Discrete And Continuous Models And Applied Computational Science 27 (2019) 305-315, http://dx.doi.org/10.22363/2658-4670-2019-27-4-305-315.
DOI: https://doi.org/10.22363/2658-4670-2019-27-4-305-315
A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly, 2019.
E. Brill, M. Banko, Scaling to very large corpora for natural language disambiguation, Proceedings of 39th Annual Meeting on Association for Computational Linguistics (2001) 26-33, https://doi.org/10.3115/1073012.1073017.
DOI: https://doi.org/10.3115/1073012.1073017
V. Shankar, R. Roelofs, H. Mania, A. Fang, B. Recht, L. Schmidt, Evaluating Machine Accuracy on ImageNet, 37th International Conference on Machine Learning (2020) 8634-8644.
E. Zuccarelli, Using machine learning to predict car accidents, https://towardsdatascience.com/using-machine-learning-to-predict-car-accidents-44664c79c942, [15.06.2021].
M. Hartley, T. S. G. Olsson, dtoolAI: Reproducibility for Deep Learning, Patterns 1(5) (2020) 100099, https://doi.org/10.1016/j.patter.2020.100073.
DOI: https://doi.org/10.1016/j.patter.2020.100073
C. Deng, X. Ji, C. Rainey, J. Zhang, W. Lu, Integrating Machine Learning with Human Knowledge, iScience 23(11) (2020) 101656, https://doi.org/10.1016/j.isci.2020.101656.
DOI: https://doi.org/10.1016/j.isci.2020.101656
Optimize TensorFlow performance using the Profiler, https://www.tensorflow.org/guide/profiler, [15.06.2021].
G. Nguyen, S. Dlugolinsky, M. Bobák, V. Tran, Á. García, I. Heredia, P. Malík, L. Hluchý, Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey, Artificial Intelligence Review 52 (2019) 77-124, https://doi.org/10.1007/s10462-018-09679-z.
DOI: https://doi.org/10.1007/s10462-018-09679-z
F. Florencio, E. D. M. Ordonez, T. V. Silva, M. C. Júnior, Performance Analysis of Deep Learning Libraries: TensorFlow and PyTorch, Journal of Computer Science 15 (2019) 785-799, http://dx.doi.org/10.3844/jcssp.2019.785.799.
DOI: https://doi.org/10.3844/jcssp.2019.785.799
Z. Ahmed, S. Amizadeh, M. Bilenko, R. Carr, W.-S. Chin, Y. Dekel, X. Dupre, V. Eksarevskiy, E. Erhardt, C. Eseanu, S. Filipi, T. Finley, A. Goswami, M. Hoover, S. Inglis, M. Interlandi, S. Katzenber, Machine Learning at Microsoft with ML.NET, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019) 2448-2458, https://doi.org/10.1145/3292500.3330667.
DOI: https://doi.org/10.1145/3292500.3330667
T. Jin, G.-T. Bercea, T. D. Le, T. Chen, G. Su, H. Imai, Y. Negishi, A. Leu, K. O'Brien, K. Kawachiya, A. E. Eichenberger, Compiling ONNX Neural Network Models Using MLIR (2020), https://arxiv.org/abs/2008.08272.
J. Redmon, YOLO: Real-Time Object Detection, https://pjreddie.com/darknet/yolov2, [10.06.2021].
J. Redmon, A. Farhadi, YOLO9000:Better, Faster, Stronger (2016), https://arxiv.org/abs/1612.08242.
DOI: https://doi.org/10.1109/CVPR.2017.690
W. Fang, L. Wang, P. Ren, Tinier-YOLO: A Real-Time Object Detection, IEEE Access 8 (2019) 1935-1944, https://doi.org/10.1109/ACCESS.2019.2961959.
DOI: https://doi.org/10.1109/ACCESS.2019.2961959