Essays about: "Neurala nätverk"
Showing result 16 - 20 of 670 essays containing the words Neurala nätverk.
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16. The impact of pruning Convolutional Neural Networks when classifying skin cancer
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Over the past few years, there have been multiple reports showcasing how Convolutional Neural Networks (CNNs) can be used to classify if skin lesions are cancerous or non-cancerous. However, a limitation of CNNs is the large number of parameters resulting in high computation times. READ MORE
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17. On Linear Mode Connectivity up to Permutation of Hidden Neurons in Neural Network : When does Weight Averaging work?
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Neural networks trained using gradient-based optimization methods exhibit a surprising phenomenon known as mode connectivity, where two independently trained network weights are not isolated low loss minima in the parameter space. Instead, they can be connected by simple curves along which the loss remains low. READ MORE
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18. Unauthorised Session Detection with RNN-LSTM Models and Topological Data Analysis
University essay from KTH/Matematik (Avd.)Abstract : This thesis explores the possibility of using session-based customers data from Svenska Handelsbanken AB to detect fraudulent sessions. Tools within Topological Data Analysis are employed to analyse customers behavior and examine topological properties such as homology and stable rank at the individual level. READ MORE
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19. Increasing explainability of neural network based retail credit risk models
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Due to their ’black box’ nature, Artificial Neural Networks (ANN) are not permitted for use in various applications. One such application is mortgage credit risk modeling. READ MORE
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20. Evaluating the Effects of Neural Noise in the Multidigraph Learning Rule
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : There exists a knowledge gap in the field of Computational Neuroscience, where many learning models for neural networks fail to take into account the influence of neural noise. The purpose of this thesis was to address this knowledge gap by investigating the robustness of the Multidigraph learning rule (MDGL) when exposed to two kinds of neural noise: external noise and internal noise. READ MORE