aws/studio-lab-examples

Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!

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SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon

Satellite Vu submission for the AWS Disaster Response hackathon

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aws-samples/aws-ml-enablement-workshop

組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ

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yoshitomo-matsubara/torchdistill

A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

Language: Python - Size: 10.5 MB - Last synced at: 29 days ago - Pushed at: about 1 month ago - Stars: 1,501 - Forks: 134

aws-samples/aws-esg-evaluation-handson

Machine Learning for ESG evaluation

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SrzStephen/DisaVu

A disaster response solution that helps allocate resources to where they're needed.

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icoxfog417/mlnote-note

機械学習帳を学ぶノート

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aws-sagemaker-jp/awesome-studio-lab-jp

SageMaker Studio Labの教材を紹介するリポジトリ。

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machinelearnear/sagemaker-studio-lab-quickstart

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machinelearnear/este-mate-no-existe

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machinelearnear/detic-detecting-20k-classes-using-image-level-supervision

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machinelearnear/large-scale-object-detection-with-sahi-detectron2

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icoxfog417/datascience-template

Data science project template

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machinelearnear/asr-restore-punctuation-summarization-biomedical-ehr

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machinelearnear/video-super-resolution-youtube

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machinelearnear/use-gradio-streamlit-sagemaker-studiolab

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machinelearnear/openai-glide-text2im

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machinelearnear/open-hf-spaces-in-studiolab

template for duplicating and executing Hugging Face Spaces either on SM Studio Lab, Google Colab, or locally.

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machinelearnear/long-audio-transcription-spanish

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machinelearnear/custom-segmentation-model-with-icevision-openimages

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machinelearnear/amazon-textract-workbench

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Ashleshk/Practical-Data-Science-on-the-AWS-Cloud-Specialization

@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

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machinelearnear/extract-info-by-doc-geometry-aws-textract

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