muhyun's Stars
aws/amazon-sagemaker-examples
Example š Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using š§ Amazon SageMaker.
facebookresearch/SlowFast
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
karpathy/arxiv-sanity-preserver
Web interface for browsing, search and filtering recent arxiv submissions
senguptaumd/Background-Matting
Background Matting: The World is Your Green Screen
data-science-on-aws/data-science-on-aws
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker
dmlc/gluon-nlp
NLP made easy
davidADSP/GDL_code
The official code repository for examples in the O'Reilly book 'Generative Deep Learning'
raminmh/liquid_time_constant_networks
Code Repository for Liquid Time-Constant Networks (LTCs)
NVIDIA-Merlin/NVTabular
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
aws-samples/aws-machine-learning-university-accelerated-tab
Machine Learning University: Accelerated Tabular Data Class
aws/deep-learning-containers
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
NVIDIA-Merlin/HugeCTR
HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
agermanidis/pigeon
š¦ Quickly annotate data from the comfort of your Jupyter notebook
aws-samples/retail-demo-store
AWS Retail Demo Store is a sample retail web application and workshop platform demonstrating how AWS infrastructure and services can be used to build compelling customer experiences for eCommerce, retail, and digital marketing use-cases
aws/sagemaker-huggingface-inference-toolkit
gyunggyung/KoGPT2-FineTuning
š„ Korean GPT-2, KoGPT2 FineTuning cased. ķźµģ“ ź°ģ¬ ė°ģ“ķ° ķģµ š„
zhanghang1989/Fast-AutoAug-Torch
aws-samples/aws-genomics-workflows
Genomics Workflows on AWS
awslabs/sagemaker-dashboards-for-ml
awslabs/ml-io
A high performance data access library for machine learning tasks
drandrewkane/AI_ML_Workshops
AI_ML_Workshops
giuseppeporcelli/sagemaker-custom-training-containers
Implementations of Amazon SageMaker-compatible custom containers for training.
aws-samples/aws-deeplens-reinvent-2019-workshops
zhanghang1989/AutoGluon-Docker
AutoGluon Docker
ishaaq/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.
aws-samples/sagemaker-gpu-performance-io-deeplearning
GPU IO optimizations for Deep Learning - Illustrated with Caltech 256 dataset on SageMaker. Here, we will be focusing on optimizations for improving I/O for GPU performance tuning. You can typically obtain 10X improvements in overall GPU training performance by just optimizing IO processing routines
hunkim/sagemaker-aihub
yashevde/BI_Project
haven-jeon/Chatbot_data
Chatbot_data_for_Korean
vdabravolski/mxnet-distributed-sample
example of distributed multi-node/multi-device training of MXNet model