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Implementation of soft dynamic time warping in pytorch
Autonomous Vehicle modelling using MATLAB and Simulink
A dependency free library of standardized optimization test functions written in pure Python.
A very simple Genetic Algorithm implementation for matlab, easy to use, easy to modify runs fast.
This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.
Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.
Implement a safe autonomous navigation in a simulated 3D environment full of cars. Apply concepts like prediction, finite state machines, behavior planning, and more.
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
Presentations And Source Codes of My Machine Learning Mini Course
building a deep neural network with as many layers as you want!
Project for "Clustering" Master course of Data Science and Information Technologies (DSIT)
š£ Cost Function interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.
A Mathematical Intuition behind Linear Regression Algorithm
Implementing the gradient descent algorithm from scratch to perform univariate linear regression to analyze the profit made by a bike sharing company.
This repository consists of Lab Assignments for course Machine Learning.
Highway Path Planner
Monte-Carlo search for the minimum of the multidimensional "cost" function
OpenLoss: This repository discloses cost functions designed for open-set classification tasks, namely, Entropic Open-set, ObjectoSphere and Maximal-Entropy Loss.
Highway Driving (project 7 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
Machine Learning
Repository for the Software and Computing for Applied Physics Project
Custom neural network implementation from scratch
Understanding hyperparameters of neural network architectures using 3 cost functions, 3 activation functions, 2 regularizations and dropout.
Grid and Graph Search with the A* algorithm (path+cost function) for a drone in an urban environment + Path optimization
The simulated annealing algorithm that minimizes a cost function, which indicates the degree of matching between the force field (FF) and density-functional theory (DFT).
Create a Deep Neural Network from Scratch using Python3.
This repository contains the lab work of the course Machine Learning (IE 406).
This repository discloses a new cost function entitled Distance Transform Loss (DTL) that punishes deep networks when class boundaries are misclassified in exchange for more accurate contour delineations, an important aspect in the geological field.
Official Implementation of our paper on Cell Tracker Algorithm for NPC Cell maturation profile understanding.
All my codes from 'Machine Learning' course by Andrew Ng offered by Coursera
This Python repository contains an example of linear regression using a single independent variable to predict a continuous dependent variable. It includes code for importing and preprocessing the data, fitting the model, and evaluating its performance. The repo also includes plots of the fitted models and analysis of the results.
Creating a Neural Network Library from scratch utilising my Mathematics background along with content covered in Andrew Ng's Deep Learning course