Muhyun Kim (muhyun)

muhyun

User data from Github https://github.com/muhyun

Company:AWS

Location:Seoul, Korea

GitHub:@muhyun


Organizations
d2l-ai

Muhyun Kim's repositories

d2l-en2kr

EN2KR on D2L - English-Korean paragraph by paragraph

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autogluon_stack_visualizer

This shows how to visualize the stack ensemble model trained by AutoGluon.

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amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

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autogluon-model-viewer

This is a Jupyter notebook based viewer of AutoGluon training job on SageMaker.

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AlignScore

ACL2023 - AlignScore, a metric for factual consistency evaluation.

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autogluon

AutoGluon: AutoML for Text, Image, and Tabular Data

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AutoGluon-Tutorial-CVPR2020

Hands-on Tutorial on Automated Deep Learning

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DeeperCluster

Implements the unsupervised pre-training of convolutional neural networks

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DeepLearningExamples

Deep Learning Examples

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dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

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distance-assistant

Pedestrian monitor that provides visual feedback to help ensure proper social distancing guidelines are being observed

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explainerdashboard

explainerdashboard is a python package for generating analytical dashboards that explain the inner workings of so-called "blackbox" machine learning models.

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grover

Code for Defending Against Neural Fake News, https://rowanzellers.com/grover/

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KoGPT2

Korean GPT-2 pretrained cased (KoGPT2)

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KorGPT2Tutorial

Tutorial for pretraining Korean GPT-2 model

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learnopencv

Learn OpenCV : C++ and Python Examples

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mxnet-distributed-sample

example of distributed multi-node/multi-device training of MXNet model

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pytorch-lightning

The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate

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ragas

Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines

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sagemaker-inference-toolkit

Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

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sagemaker-pipe

This project builds a very simple implementation of SageMaker Training's internal IO subsystem that is able to pipe channel data files to an algorithm. It is meant to be used as a local-testing tool in order to test a PIPE mode algorithm locally before attempting to run it for real with SageMaker Training.

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sagemaker-pytorch-container

Docker container for running PyTorch scripts to train and host PyTorch models on SageMaker

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serve

Model Serving on PyTorch

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streamlit

Streamlit — The fastest way to build data apps in Python

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

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WWW20-Hands-on-Tutorial

Materials for DGL hands-on tutorial in WWW 2020

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