Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm
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Updated
Jun 7, 2021 - Jupyter Notebook
Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm
Global L2 norm adaptive gradient clipping engine to mitigate exploding gradients during deep neural network training.
Global L2 norm adaptive gradient clipping engine to mitigate exploding gradients during deep neural network training.
Project developed for the exam of Biometric Systems. Application that uses machine learning algorithms to identify and recognize the user based on typing patterns on the keyboard
Collection of Generic Data Structures and Algorithms in C.
An implementation of neural network with regularization
Course assignment for Algorithm and Massive Datasets comparing SAD and SSD for motion estimation. Includes analysis of accuracy, speed (Python/NumPy), and error sensitivity. Highlights trade-offs for speed-critical vs. precision-focused applications.
Compute the L2-norm of a one-dimensional double-precision complex floating-point ndarray.
Game of life triggered by movement on discretised subsequent frames of a video
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