This tool provides an easy way to generate a preferences profile of a given Twitter user.
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Updated
Jun 22, 2022 - Python
This tool provides an easy way to generate a preferences profile of a given Twitter user.
App para detetar e separar maçãs em pequenas, grandes ou defeituosas. / App to detect and sort apples in small, big or defective.
In this repository, we deal with the FashionMNIST classification using deep multilayer perceptron (MLP) models as well as using deep convolutional neural networks (CNN) models.
Software to automate the categorization and classification of code-snippets.
This repo contains a Python script that will go through the user reviews from an app and generate insight for the app creator. This is done by categorising each review into "positive", "negative", or "neutral". The insight will contain the distribution of reviews across these 3 categories so the creator knows how well the app is doing at a glanc…
This project determines the sentiment of each tweet and the cluster classifies the same into four categories.
Create a fully connected neural network with Zalando's Fashion MNIST image data sets
Poncho: Uncategorized Website Management Tool for Cisco Umbrella that uses OpenAI
FashionMnist competition repository
MIRROR of https://codeberg.org/catseye/tagfarm : An ultra-lightweight filesystem-based categorization system for arbitrary files
Discover who really is your friend on social media, and get rid of parasites who do not follow back (analyzer bot)
Website Categorization API client library for Python
Automation of Prioritization and Categorization of Support Tickets Using LLMs and Vector DBs
🐹 Categorizing pokémon by its inspiration origin
An interactive platform for collecting user reviews of different art forms, categorising them into "Books", "Movies" and "Music" and calculating an average rating for each artwork based on user reviews, powered by Django and DjangoRestFramework.
A Streamlit app based on Python that fetches top news articles from the News API, generates a summary of each article using the OpenAI GPT-3 model, analyzes the sentiment of the article using the NLTK library, and classifies the article into different categories based on keywords.
Categorize weight as normal, below average, or above average based on criteria for females (0-50 kg thin, 51-70 kg normal, 71-100 kg heavy, >100 kg obese) and males (0-60 kg thin, 61-80 kg normal, 81-100 kg heavy, >110 kg obese) by inputting sex and weight. Program uses if-else statements to determine appropriate category.
A small tool to categorize and check file integerity after data recovery with photorec
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