a gradient-based optimisation routine for highly parameterised non-linear dynamical models
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Updated
May 13, 2024 - MATLAB
a gradient-based optimisation routine for highly parameterised non-linear dynamical models
A.K.A. NUS ME5411 Final Project. Implemented a CNN framework without off-the-shelf libraries and its application for character recognition.
Labs of DSP2
Primal-Dual algorithm for smooth regularization of non-smooth optimization functions
Implementation of basic optimization algorithms, in trivial problems that were written for educational purposes on Convex Optimization course at TUC
This Repository contains Solutions to Lab Assignments/slides and my personal Notes of the Machine Learning (2022) from Stanford University on Coursera taught by Andrew Ng.
My solutions to the programming assignments on Andrew Ng's course on ML.
Implementations of various Algorithms used in Numerical Analysis, from root-finding up to gradient descent and numerically solving PDEs.
Linear Regression using Matlab on a Kaggle dataset.
Animating how Adaline classification works by minimizing cost. Showing comparison of three kinds of gradient descent.
Projects from the Robotics specialization from Coursera offered by the University of Pennsylvania
Machine learning and more using matlab
NMFLibrary: Non-negative Matrix Factorization (NMF) Library: Version 2.1
Machine Learning and Analysis of Big Data course, Computer Science M.Sc., Ben Gurion University of the Negev, 2020
Animation of how Gradient Descent finds the local minimum. Demonstrate how learning rate and starting points affect Gradient Descent.
The implementation of advanced mathematical optimization methods
Controlling a nonholonomic robot to follow a trajectory with a modified PID Controller.
A simple example of the Gradient-Descent algorithm and a simple implementation of ADMM for LASSO.(Homework of the large-scale optimization course.)
Optimisation and algorithm project. I) L1, L2, and L2^2 regularisers in optimal trajectory synthesis; II) Logistic data classification; III) Gradient methods.
Quantitative Engineering Analysis 1 | Spring 2020 | Path Planning using Gradient Descent
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