Podręcznik

1.     Bengio Y., LeCun Y., Hinton G., Deep Learning, Nature, 2015, vol. 521, pp. 436–444.

2.     Brownlee J., Deep Learning for Natural Language Processing. Develop Deep Learning Models for your Natural Language Problems, Ebook, 2018.

3.     Breiman L., Random forests, Machine Learning, 2001, vol. 45, No 11, pp. 5–32.

4.     Banerjee S., Linear algebra and matrix analysis for statistics, 2012, London.

5.     Brudzewski K., Osowski S., Markiewicz T., Ulaczyk J., Classification of gasoline with supplement of bio-products by means of an electronic nose and SVM neural network, Sensors and Actuators - Chemical, 2006, vol. 113, No 1, pp. 135-141.

6.     Chen S., Cowan C.F., Grant P.M., Orthogonal least squares learning algorithm for radial basis function networks, IEEE Trans. Neural Networks, 1991, vol. 2, pp. 302–309.

7.     Christensen R., Johnson W. O., Branscum A. J., Hanson T. E., Bayesian ideas and data analysis: an introduction for scientists and statisticians, 2010, Chapman & Hall/CRC Science

8.     Cichocki A., Amari S. I., Adaptive blind signal and image processing, 2003, Wiley, New York.

9.     Crammer K. , Singer Y., On the learnability and design of output codes for multiclass problems. Computational Learning Theory, 2000, pp. 35-46.

10.  Duda, R.O., Hart, P.E., Stork, P., Pattern classification and scene analysis, 2003, Wiley, New York.

11.  Fogel, L.J., Intelligence through simulated evolution : forty years of evolutionary programming, 1999, Wiley, New York.

12.  Fukushima K.: Neocognitron - a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 1980, vol.. 36, No 4, pp. 193–202, doi:10.1007/bf00344251.

13.  Genc H., Cataltepe Z., Pearson T., A new PCA/ICA based feature selection method, IEEE 15th In Signal Processing and Communications Applications, 2007, pp. 1-4.

14.  Gill P., Murray W., Wright M., Practical optimization, 1981, Academic Press, London.

15.  Goldberg D., Algorytmy genetyczne i ich zastosowania, 2003, WNT Warszawa.

16.  Golub G., Van Loan C., Matrix computations, 1996, John Hopkins University Press, Baltimore.

17.  Gao H., Liu Z., Van der Maaten L., Weinberger K., Densely connected convolutional networks,  CVPR, vol. 1, no. 2, p. 3. 2017.

18.  Goodfellow I., Bengio Y., Courville A.: Deep learning 2016, MIT Press, Massachusetts (tłumaczenie polskie: Deep Learning. Współczesne systemy uczące się, Helion, Gliwice, 2018).

19.  Goodfellow I., Pouget-Abadie J., Mirza M, Xu M., Warde-Farley B., Ozair D., Courville A. Bengio Y., Generative Adversarial Nets (PDF). Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672–2680.

20.  Greff K., Srivastava R. K., Koutník J., Steunebrink B. R., Schmidhuber J., LSTM: A search space odyssey, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, No 10, pp. 2222-2232, 2017.

21.  Gunn S., Support vector machines for classification and regression, ISIS Technical report, 1998, University of Southampton.

22.  Guyon I., Elisseeff A., An introduction to variable and feature selection, J. Mach. Learn. Res., 2003, vol. 3, pp. 1157-1182.

23.  Guyon, I., Weston, J., Barnhill, S., Vapnik, V., Gene selection for cancer classification using Support Vector Machines, Machine Learning, 2002, vol. 46, pp. 389-422.

24.  Haykin S., Neural networks, a comprehensive foundation, Macmillan College Publishing Company, 2000, New York.

25.  He K., Zhang X, Ren S, Sun J., Deep Residual Learning for Image Recognition, 2015, http://arxiv.org/abs/1512.03385.

26.  Hinton G. E., Salakhutdinov R. R., Reducing the dimensionality of data with neural networks, Science, 313:504-507, 2006.

27.  Howard A., Zhu M., Chen B., Kalenichenko D., MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv: 1704.04861v1 [cs.CV], 2017.

28.  Hsu, C.W., Lin, C.J., A comparison methods for multi class support vector machines, IEEE Trans. Neural Networks, 2002, vol. 13, pp. 415-425.

29.  Huang G., Liu Z., van der Maaten L., Weinberger K., Densely connected convolutional networks, arXiv: 1606.06993v5 [cs.CV] 2018.

30.  Iandola F, Han S., Moskevicz M., Ashraf K., Dally W., Keutzer K, Squeezenet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, Conference ICLR, 20017. pp. 1-13.

31.  Joachims T., Making large scale SVM learning practical, (in ”Advances in kernel methods - support vector learning”, B. Scholkopf, C. Burges, A. Smola eds). MIT Press, Cambridge, 1998, pp. 41-56.

32.  Kecman V., Support vector machines, neural networks and fuzzy logic models, 2001, Cambridge, MA: MIT Press.

33.  Kingma P, Welling M., An introduction to variational autoencoders, Foundations and Trends in Machine  Learning, 12:307-392, 2019.

34.  Krizhevsky A., Sutskever I., Hinton G., Image net classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol. 25, pp. 1-9, 2012.

35.  Kruk M., Świderski B., Osowski S., Kurek J., Słowińska M., Walecka I., Melanoma recognition using extended set of descriptors and classifiers, Eurasip Journal on Image and Video Processing, 2015, vol. 43, pp. 1-10,  DOI 10.1186/s13640-015-0099-9

36.  Kuncheva L., Combining pattern classifiers: methods and algorithms, 2015, Wiley, New York.

37.  LeCun Y., Bengio Y., Convolutional networks for images, speech, and time-series. 1995, in Arbib M. A. (editor), The Handbook of Brain Theory and Neural Networks. MIT Press, Massachusetts.

38.  Lecture CS231n: 2017, ‎Stanford Vision Lab, ‎Stanford University.

39.   Lee K.C., Han I., Kwon Y., Hybrid Neural Network models for bankruptcy prediction, Decision Support Systems, 18 (1996) 63-72.

40.  Leś T., Osowski S., Kruk M., Automatic recognition of industrial tools using artificial intelligence approach, Expert Systems with Applications, 2013, vol. 40, pp. 4777-4784.

41.  Lin C. J., Chang, C. C., LIBSVM: a library for support vector machines. http://www. csie. ntu. edu. tw/cjlin/libsvm

42.  Markiewicz T., Sieci neuronowe SVM w zastosowaniu do klasyfikacji obrazów komórek szpiku kostnego, rozprawa doktorska Politechniki Warszawskiej, 2006.

43.  Matlab user manual MathWorks, 2021, Natick, USA.

44.  Michalewicz Z., Algorytmy genetyczne + struktury danych = programy ewolucyjne, WNT, Warszawa 1996.

45.  Osowski S., Cichocki A., Siwek K., Matlab w zastosowaniu do obliczeń obwodowych i przetwarzania sygnałów, 2006, Oficyna Wydawnicza PW.

46.  Osowski S., Sieci neuronowe do przetwarzania informacji, 2020, Oficyna Wydawnicza PW.

47.  Osowski S., Szmurło R., Siwek K., Ciechulski T., Neural approaches to short-time load forecasting in power systems – a comparative study, Energies, 2022, 15,  pp. 3265.

48.  Osowski S., Siwek K., R. Szupiluk, Ensemble neural network approach for accurate load forecasting in the power system, Applied Mathematics and Computer Science, 2009, vol.19, No.2, pp. 303-315.

49.  Osowski S., Metody i narzędzia eksploracji danych , Wydawnictwo BTC, Warszawa, 2013

50.  Patterson J., Gibson A., Deep Learning: A Practitioner's Approach (tłumaczenie polskie :  Deep learning. Praktyczne wprowadzenie), Helion, Gliwice, 2018.

51.  Platt L., Fast training of SVM using sequential optimization (in Scholkopf, B., Burges, B., Smola, A., Eds. Advances in kernel methods – support vector learning. Cambridge: MIT Press), 1998, 185-208.

52.  Redmon J., Divval S., Girshick R., Farhafi A., You Only Look Once: unified. Real time object detection, axXiv: 1506.02640v5 [cs.CV], 2016

53.  Ren S., He K., Girshick R., Sun J., Faster R-CNN: toward real time object detection with region proposal networks, IEEE trans. PAMI, vol. 39, pp. 1137-1149, 2017

54.  Riedmiller M., Braun H.: RPROP – a fast adaptive learning algorithm. Technical Report, University Karlsruhe, Karlsruhe 1992.

55.  Ridgeway G., Generalized Boosted Models: A guide to the gbm package. 2007

56.   Ronneberger O., Fischer P., Brox T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015, arXiv:1505.04597.

57.  Sammon J. W., A nonlinear mapping for data structure analysis, IEEE Trans. on Computers, 1969, vol. 18, pp. 401-409.

58.  Schmidhuber J., Deep learning in neural networks: An overview, Neural Networks, vol. 61, pp. 85-117, 2015.

59.  Schölkopf B., Smola A., Learning with kernels,  2002, MIT Press, Cambridge MA.

60.  Schurmann J., Pattern classification, a unified view of statistical and neural approaches, 1996, Wiley, New York.

61.  Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.C. "MobileNetV2: Inverted Residuals and Linear Bottlenecks." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4510-4520). IEEE.

62.  Swiderski B., Kurek J., Osowski S., Multistage classification by using logistic regression and neural networks for assessment of financial condition of company, Decision Support Systems, 2012, vol. 52, No 2, pp. 539-547

63.  Szegedy C, Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., Going deeper with convolutions, arXiv: 1409.4842v1, 2014.

64.  Szegedy C, Ioffe S., Vanhoucke V. Inveption-v4, Inception-ResNet and the impact of residual connections on learning, arXiv:1602.07261v2, 2016.

65.  Tan P.N., Steinbach M., Kumar V., Introduction to data mining, 2006, Pearson Education Inc., Boston.

66.  Tan M. Le Q., EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, arXiv:1905.11946 [cs.LG], 2020

67.  Van der Maaten L., Hinton G., Visualising data using t-SNE, Journal of Machine Learning Research, 2008, vol. 9, pp. 2579-2602.

68.  Vapnik V., Statistical learning theory, 1998,  Wiley, New York.

69.  Wagner T., Texture analysis (w Jahne, B., Haussecker, H., Geisser, P., Eds. Handbook of computer vision and application. Boston: Academic Press), 1999, ss. 275-309.

70.  Zeiler M. D., Fergus R.: Visualizing and Understanding Convolutional Networks. 2013, pp. 1-11, https://arxiv.org/abs/1311.2901.

71.  Zhang X., Zhou X., Lin M., Sun J., ShuffleNet: an extremely efficient convolutional neural network for mobile devices, arXiv: 1707.01083v2 [cs.CV], 2017.

72.  Zheng G., Liu S., Zeming F. W., Sun L. J., YOLOX: Exceeding YOLO Series in 2021, arXiv:2107.08430v2 [cs.CV]

73.  https://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/

74.  http://www.bsp.brain.riken.jp/ICALAB, ICALAB Toolboxes. A. Cichocki, S. Amari, K. Siwek, T. Tanaka et al.

75.  https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet.

76.  https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73

77.  https://www.jeremyjordan.me/variational-autoencoders/

78.  Osowski S., Szmurło R., Matematyczne modele uczenia maszynowego w językach matlab i Python, OWPW, 2023, Warszawa.