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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Posts
Convolution and Pooling operation feature map shape calculator.
Published:
This post allows users calculate/track the feature map size while stacking the Convolution and Pooling operations and select the appropriate number of neurons in the first Fully Connected/ Dense/Linear layer while building Convolutional Neural Networks.
Understanding the Nuts and Bolts of Convolutional Neural Networks: Insights and from-scratch implementation using PyTorch - Part 2.
Published:
This post provides an in-depth understanding of Convolutional Neural Networks (CNNs), detailed insights into bedrock operations that power CNNs and step-by-step implementation using PyTorch.
Understanding the Nuts and Bolts of Convolutional Neural Networks: Insights and from-scratch implementation using PyTorch - Part 1.
Published:
This post provides an in-depth understanding of Convolutional Neural Networks (CNNs), detailed insights into bedrock operations that power CNNs and step-by-step implementation using PyTorch.
Pooling operation feature map calculator.
Published:
This post allows users to experiment with 2D pooling by defining input data shape, values, and pooling filter/kernel shape. It will calculate the feature map size and values for the given input based on max or average pooling.
Convolution operation feature map calculator.
Published:
This post allows users to experiment with 2D convolution by defining input data shape, values, filter/kernel shape, values, and other parameters like stride and padding. It will calculate the feature map size and values for the given input and kernel values.
Mastering Multilayer Perceptrons: understanding forward and backpropagation with numerical example using Python.
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This post discusses Neural Networks, particularly Multilayer Perceptron (MLP), and delves with a step-by-step numerical example for forward and backpropagation along with its Python implementation.
Complete Logistic Regression tutorial using Python: Understanding mathematics, building, and interpreting classification models.
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This post provides an in-detail discussion of the Logistic Regression algorithm with Real-World example and its implementation from scratch using Python.
Complete tutorial on Linear Regression using Python: theory, implementation with real-world example.
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This post provides an in-depth discussion of the Linear Regression algorithm and its implementation from scratch using Python.
Introduction to Machine Learning
Published:
This post will give a brief introduction to Machine Learning.
publications
Design And Efficient Deployment of Honeypot and Dynamic Rule Based Live Network Intrusion Collaborative System
Prasad, Renuka B., Annamma Abraham, Abhas Abhinav, Sunil V. Gurlahosur, and Y. Srinivasa. "Design and Efficient Deployment of Honeypot and Dynamic Rule Based Live Network Intrusion Collaborative System." International Journal of Network Security & its Applications 3, no. 2 (2011).
Predicting clicks: CTR estimation of advertisements using Logistic Regression classifier
R. Kumar, S. M. Naik, V. D. Naik, S. Shiralli, Sunil V.G and M. Husain, "Predicting clicks: CTR estimation of advertisements using Logistic Regression classifier," 2015 IEEE International Advance Computing Conference (IACC), Banglore, India, 2015, pp. 1134-1138, doi: 10.1109/IADCC.2015.7154880.
Classification of Cultural Heritage Sites Using Transfer Learning
U. Kulkarni, S. M. Meena, S. V. Gurlahosur and U. Mudengudi, "Classification of Cultural Heritage Sites Using Transfer Learning," 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), Singapore, 2019, pp. 391-397, doi: 10.1109/BigMM.2019.00020.
Integrated Crowdsourcing Framework Using Deep Learning for Digitalization of Indian Heritage Infrastructure
U. Kulkarni, S. M. Meena, P. Joshua, K. Rodrigues and S. V. Gurlahosur, "Integrated Crowdsourcing Framework Using Deep Learning for Digitalization of Indian Heritage Infrastructure," 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), New Delhi, India, 2020, pp. 200-208, doi: 10.1109/BigMM50055.2020.00036.
Crowd Source Framework for Indian Digital Heritage Space
Meena, S.M., Abhishek, N.K., Ravikumar, A., Kulkarni, U., Gurlahosur, S.V., Uma, M. (2021). Crowd Source Framework for Indian Digital Heritage Space. In: Belhi, A., Bouras, A., Al-Ali, A.K., Sadka, A.H. (eds) Data Analytics for Cultural Heritage. Springer, Cham. https://doi.org/10.1007/978-3-030-66777-1_6
2D-Image Super-Resolution on Heritage Site
Pyatigoudar, S., Meena, S.M., Gurlahosur, S.V., Kulkarni, U. (2021). 2D-Image Super-Resolution on Heritage Site. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 736. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0_34
Optimization of multi-objectives for Adaptive Reverse Recommendation method on edge devices
W. Dcosta, M. S. M, S. V. Gurlahosur and U. Kulkarni, "Optimization of multi-objectives for Adaptive Reverse Recommendation method on edge devices," 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2022, pp. 1-6, doi: 10.1109/ICCCNT54827.2022.9984572.
Comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites
Gurlahosur, S.V. et al. (2022). Comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites. In: Mudenagudi, U., Nigam, A., Sarvadevabhatla, R.K., Choudhary, A. (eds) Proceedings of the Satellite Workshops of ICVGIP 2021. Lecture Notes in Electrical Engineering, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-19-4136-8_2