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angrycaptain19's Projects

genasweb icon genasweb

Web version of my Genshin Artifact Simulator.

genetic-snake icon genetic-snake

Utiliser un réseau de neurone (FeedForward) pour permettre un apprentissage machine à l’aide d’un algorithme génétique, en utilisant différents scénarios afin de comparer le résultat final des différents modèles.

genos icon genos

Instantiate objects and call functions using dictionary configs in Python using Genos.

git-sweep icon git-sweep

A command-line tool that helps you clean up Git branches that have been merged into master.

gluon-ts icon gluon-ts

Probabilistic time series modeling in Python

gooey icon gooey

Turn (almost) any Python command line program into a full GUI application with one line

gpt-2 icon gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

gpuweb icon gpuweb

Where the GPU for the Web work happens!

gym icon gym

A toolkit for developing and comparing reinforcement learning algorithms.

hail icon hail

Scalable genomic data analysis.

haystack icon haystack

:mag: Haystack is an open source NLP framework that leverages Transformer models. It enables developers to implement production-ready neural search, question answering, semantic document search and summarization for a wide range of applications.

httpbin icon httpbin

HTTP Request & Response Service, written in Python + Flask.

httpie icon httpie

As easy as /aitch-tee-tee-pie/ 🥧 Modern, user-friendly command-line HTTP client for the API era. JSON support, colors, sessions, downloads, plugins & more. https://twitter.com/httpie

hub icon hub

Dataset format for AI. Build, manage, & visualize datasets for deep learning. Stream data real-time to PyTorch/TensorFlow & version-control it. https://activeloop.ai

hue icon hue

Open source SQL Query Assistant service for Databases/Warehouses

hummingbot icon hummingbot

Hummingbot is open source software that helps you build trading bots that run on any exchange or blockchain

ignite icon ignite

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

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