{"id":13739017,"url":"https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp","last_synced_at":"2025-05-08T18:32:07.320Z","repository":{"id":96775596,"uuid":"192028436","full_name":"Machine-Learning-Tokyo/ELSI-DL-Bootcamp","owner":"Machine-Learning-Tokyo","description":"Intro to Machine Learning and Deep Learning for Earth-Life Sciences","archived":false,"fork":false,"pushed_at":"2019-06-29T08:35:30.000Z","size":20975,"stargazers_count":14,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-28T13:58:25.766Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Machine-Learning-Tokyo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2019-06-15T02:40:22.000Z","updated_at":"2023-04-17T11:29:52.000Z","dependencies_parsed_at":null,"dependency_job_id":"0eca5666-a963-46d5-9fd7-1c14917990ef","html_url":"https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FELSI-DL-Bootcamp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FELSI-DL-Bootcamp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FELSI-DL-Bootcamp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FELSI-DL-Bootcamp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Machine-Learning-Tokyo","download_url":"https://codeload.github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253127132,"owners_count":21858196,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T04:00:23.208Z","updated_at":"2025-05-08T18:32:02.273Z","avatar_url":"https://github.com/Machine-Learning-Tokyo.png","language":"Jupyter Notebook","funding_links":[],"categories":["Tutorials"],"sub_categories":[],"readme":"# ELSI-DL-Bootcamp\nIntro to Machine Learning and Deep Learning for Earth-Life Sciences\n\n## Slides\n\n### [ML Research Project Management](https://docs.google.com/presentation/d/1y4v1WdDILWbbqPQzEO8W4v33dVoCFl5I_04dTFyJZoE/edit?usp=sharing)\n\n### [Intro to Deep Learning](https://docs.google.com/presentation/d/1V-O6DAKWkRUGpBT2PvB5LQ2X1BJMUOxwZupKhLQpXb8/edit?usp=sharing)\n### [Intro to Convolutional Neural Networks](https://docs.google.com/presentation/d/1Z27oJAUO_mUQWcZDyl5nu4MLPk7FF8ggeAqLdyOrIMU/edit?usp=sharing)\n\n## Notebooks\n\n### [Exploratory Data Analysis](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/Data_Exploration.ipynb)\n### [Data Visualization](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/data_visualization.ipynb)\n### [Train a Convolutional Neural Network](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/kaggle_sat.ipynb)\n\n\n**Data**: Kaggle - [DeepSat (SAT-6) Airborne Dataset](https://www.kaggle.com/crawford/deepsat-sat6)\n\n405,000 image patches each of size 28x28 and covering 6 landcover classes\n\n**Content**\n- Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared.\n- The training and test labels are one-hot encoded 1x6 vectors\n- The six classes represent the six broad land covers which include barren land, trees, grassland, roads, buildings and water bodies.\n- Training and test datasets belong to disjoint set of image tiles.\n- Each image patch is size normalized to 28x28 pixels.\n- Once generated, both the training and testing datasets were randomized using a pseudo-random number generator.\n\n[\u003cp align=\"center\"\u003e\u003cimg src=\"https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/deepsat.png\" width=\"600\"\u003e\u003c/p\u003e](https://www.kaggle.com/crawford/deepsat-sat6)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMachine-Learning-Tokyo%2FELSI-DL-Bootcamp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMachine-Learning-Tokyo%2FELSI-DL-Bootcamp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMachine-Learning-Tokyo%2FELSI-DL-Bootcamp/lists"}