Clustering with dynamic neural network
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Updated
Nov 1, 2023 - Python
Clustering with dynamic neural network
Simple closest pair problem solution with python 🧩
A Banking Dataset is taken from Kaggle and applied 3 of the most used naive bayes formulas to get the accuracy
Agglomerative Hierarchical Clustering with Centroid method and Euclidean Distance
A python package to implement all different distance/routing methods (Great Circle/Rhumbline/Haversine/Eucledian).
An academic project to find the most similar image to the given input image, based on Image Processing, Cosine Similarity Model, StreamLit, written primarily in Python using Visual Studio Code and Jupyter Notebook
In this repo i have tried to explain how to calculate Euclidean Distance,manhattan distance, and Finally Calculating the Dissimilarity Matrix/Distance Matrix using python.
Parallel-Indexed multi-dimensional query engine for efficient vector search
Implementation (VHDL) and verification of the accelerator proposed in the paper "Hardware Accelerator for Shapelet Distance Computation in Time-Series Classification", from May 2020
8-Puzzle solver implemented using search algorithms: DFS, BFS, A-Star (Manhattan and Euclidean heuristics) with GUI for user interactivity
Content-based recommendation engine using Python and Scikitlearn, using concepts of Cosine distance and Euclidean distance. Finally, by using IMDB 5000 movie dataset built a content-based recommendation engine using CountVectorize and Cosine similarity scores between movies.
KDTree-based K-Nearest Neighbor graph implementation using python
MNIST_WITHOUT_SKLEARN: The MNIST_Scipy.py module is presented which, using the Scipy library, is applied to the recognition of handwritten characters contained in the MNIST file, achieving a similar hit rate than the module referenced at https://github.com/ablanco1950 / MNIST_KNN Although with a longer execution time.
Product Recommendation Engine Recommendation engines are now a one of the most common Machine Learning project that can be seen now-a-days. In fact, some biggest brands are build around one, like Netflix, Amazon, Google, etc. Thirty-five percent of purchases on Amazon come from product recommendations.
Cluster your data using the euclidean distance and watch the distance matrix for each epoch of the algorithm. The program reads the data by a .csv file and plots the results on dendrogram and radar plots.
In this Project, we are challenged to build a model that predicts the total ride duration of taxi trips in New York City
NN-Descent Implementation in python using the nmslib library
Java Program Designed to recognise a digit from a 8*8 grid of numbers, Categorisation Task, Completed using 2 Solutions. Euclidean Distance and Self Organising Maps.
This project is an implementation of K-Means clustering that using a random walk based distance measure
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