Grid and Graph Search with the A* algorithm (path+cost function) for a drone in an urban environment + Path optimization
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Updated
Feb 3, 2020 - Python
Grid and Graph Search with the A* algorithm (path+cost function) for a drone in an urban environment + Path optimization
Implemented gradient descent and cost function for logistic regression from scratch using numpy
Machine_Learning_Stanford_Ng
OpenLoss: This repository discloses cost functions designed for open-set classification tasks, namely, Entropic Open-set, ObjectoSphere and Maximal-Entropy Loss.
this repository is for the cosmology course in the Winter of 1401. you can find out your computational homework here.
stanford university
Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation
Implement DFS, BFS, UCS, and A* algorithms && minimax and expectimax algorithms, as well as designing evaluation functions
With OCTAVE - details in ex2.pdf in the repo.
Handwritten Digit Recognition - Neural Network - minimizing the cost function (Backpropagation) ---- OCTAVE ----- the exercise details are in ex4.pdf in the repo.
This repository provides a comprehensive machine learning course with theoretical concepts and practical implementations
Implementation of necessary supervised machine learning algorithms for regression and classification.
🧮 Calculate Cost Furniture, Windows and Doors
Machine Learning from Coursera: https://www.coursera.org/learn/machine-learning/home/welcome
Implemented gradient descent in linear regression from scratch using numpy
The code of forward propagation , cost function , backpropagation and visualize the hidden layer.
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