Calculation and visualization of molecular networks based on t-SNE algorithm
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Updated
Jun 6, 2024 - Python
Calculation and visualization of molecular networks based on t-SNE algorithm
this project to developed a Python
Performing K-means clustering on MNIST data from scratch. Instead of using Euclidean distance metric, I have used Cosine Similarity as distance metric. Clustering is done in 10, 7, and 4 clusters for analysis.
Here we have fully implemented a number of algorithms related to machine learning
A .NET port of java-string-similarity
Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. In-memory with optional persistence.
FAst Lookups of Cosine and Other Nearest Neighbors (based on fast locality-sensitive hashing)
exploration content-based and collaborative filtering
Example to help understand how to use hugging face sentance_transformers to encode searchable text into embeddings AND how to use cosine similarity search to determine similarity between a search prompt and the embeddings
Simple statistical functions that are useful for exploratory spatial data analysis (ESDA) on-the-fly in JavaScript
Movie Recommender System
An app to match the resume and a list of company information
Simple chatbot (NLP ONLY without machine learning) using Levenshtein Distance + TF-IDF + Cosine Similiarity :D
This project is designed to streamline the recruitment process by providing a job and resume matching system and a chatbot for applicants. The key functionalities include: Job and Resume Matching and LLM powered chatbot
Source code for personalised github repository recommendation using keyword extraction and similarity index matching
Movie Recommender
Movie recommendation using cosine similarity
Python package to accelerate the sparse matrix multiplication and top-n similarity selection
This repository is a related to all about Natural Langauge Processing - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)
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