EvoLandEco

Tianjian's starred repositories

hello-algo

《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing

Language:JavaNOASSERTION88422 515 202

arrow

Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

Language:C++Apache-2.013964 354 25208

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Language:Java12562 403 18

optuna

A hyperparameter optimization framework

Language:PythonNOASSERTION10226 118 1649

eww

ElKowars wacky widgets

Language:RustMIT8846 45 687

ViVe

C# library and console app for using new feature control APIs available in Windows 10 version 2004 and newer

Language:C#GPL-3.04785 83 126

brms

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

Language:RGPL-2.01254 47 1467

tgn

TGN: Temporal Graph Networks

Language:PythonApache-2.0917 24 30

collapse

Advanced and Fast Data Transformation in R

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geomtextpath

Create curved text paths in ggplot2

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disk.frame

Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data

Language:RNOASSERTION593 20 229

That's a terminal prompt changer that shows a few cool git informations about your repository state. You can choose among different styles and enjoy all the aliases it provides you. Feel free for contributing, pull requests and issues are always welcome! ;)

Language:ShellGPL-2.0469 9 60

GRAN

Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019

Language:C++MIT457 11 21

Thor

SFML Extension with various game programming features, like particles, animations, vector operations

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neural-subgraph-learning-GNN

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microeco

An R package for data analysis in microbial community ecology

Language:RGPL-3.0182 4 339

Rfast

A collection of Rfast functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results. Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.

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