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Masa's starred repositories

llama.cpp

LLM inference in C/C++

Language:C++MIT6284000

Bayesian-causal-inference

Tutorials on Stan for Bayesian causal inference

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deeplearning4j-examples

Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)

Language:JavaNOASSERTION244100

openai-cookbook

Examples and guides for using the OpenAI API

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RStanBook

『StanとRでベイズ統計モデリング』のサポートページです.

Language:R20600

Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...

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stan

Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

Language:C++BSD-3-Clause255900

BMS

『社会科学のためのベイズ統計モデリング』サポーサイト

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simplenote-backup

A Python program to backup your simplenote.com

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polars

Dataframes powered by a multithreaded, vectorized query engine, written in Rust

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PyClick

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deep-ctr

An attempt of training DNN models to predict ad click-through rate, implemented with Theano.

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clickmodels

ClickModels is a small set of Python scripts for the user click models initially developed at Yandex. A Click Model is a probabilistic graphical model used to predict search engine click data from past observations. This project is aimed to deal with click models used in Information Retrieval (see next README.md) and intended to be easy-to-read and easy-to-modify. If it's not, please let me know how to improve it :)

Language:PythonBSD-3-Clause23400

tree-sitter

An incremental parsing system for programming tools

Language:RustMIT1748300

emacs

Mirror of https://git.savannah.gnu.org/cgit/emacs.git

Language:Emacs LispGPL-3.0400

emacs-writing-studio

Emacs configuration for authors who research, write and publish articles, books and websites.

Language:TeXGPL-3.030100

baidu_ultr_dataset

an unbias-learning-to-rank dataset of Baidu

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AnoShift

Language:PythonBSD-3-Clause4400

evaluating-eeg-representations

Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.

MIT1500