{"id":19068724,"url":"https://github.com/machine-learning-tokyo/edge_ai","last_synced_at":"2026-05-16T16:30:14.501Z","repository":{"id":96775656,"uuid":"188495349","full_name":"Machine-Learning-Tokyo/Edge_AI","owner":"Machine-Learning-Tokyo","description":"Resources for our Workshops on Edge AI","archived":false,"fork":false,"pushed_at":"2019-05-26T02:46:06.000Z","size":42209,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-02T15:27:04.186Z","etag":null,"topics":["edge-ai","embedded-machine-learning"],"latest_commit_sha":null,"homepage":null,"language":null,"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,"publiccode":null,"codemeta":null}},"created_at":"2019-05-24T22:39:26.000Z","updated_at":"2021-07-07T04:05:30.000Z","dependencies_parsed_at":null,"dependency_job_id":"4a7a0e37-7ba2-471b-a1f2-de35da757f19","html_url":"https://github.com/Machine-Learning-Tokyo/Edge_AI","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%2FEdge_AI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FEdge_AI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FEdge_AI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FEdge_AI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Machine-Learning-Tokyo","download_url":"https://codeload.github.com/Machine-Learning-Tokyo/Edge_AI/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240122476,"owners_count":19751140,"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":["edge-ai","embedded-machine-learning"],"created_at":"2024-11-09T01:11:33.719Z","updated_at":"2026-05-16T16:30:14.074Z","avatar_url":"https://github.com/Machine-Learning-Tokyo.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# MLT Edge AI\nWe are excited to kick off our first Embedded ML workshop - a combination of presentation and ideathon.\nhttps://www.meetup.com/Machine-Learning-Tokyo/\n\nFirstly, we will talk about the promise of light-weight deep neural networks for energy-efficient and low-cost IoT applications. We discuss some examples of accelerated and low-memory version of deep learning models for real-time use, predictive maintenance, time-series analysis, and demand forecast. We focus on AI methods for turning IoT data into insights and actions.\n\nAfter the introductory talk about Edge AI we will build teams of 3-5 people and have 1.5 hours to come up with project ideas for embedded ML scenarios that include use case, feasibility assessment, workflow, allocation of resources (human, time, computation, ...).\n\n-- AGENDA --\n- 12:30 pm Doors open\n- 1:00 pm - 1:45 pm Talk: \"AI for Embedded Computing: Towards low-power Edge Inference\", Hossein I. Rad\n- 1:45 pm - 2:00 pm Edge AI Ideathon, Yoovraj Shinde\n- 2:00 pm - 2:45 pm Team building break with snacks and drinks\n- 2:45 pm - 4:15 pm Ideathon\n- 4:15 pm - 5:00 pm Presentations\n- 5:00 pm Wrap up\n\n-- MLT EDGE AI TEAM --\n\nHossein Izadi Rad is a Ph.D. candidate of Information Science and Technology with the University of Tokyo. He has an M.E. degree in Electrical Engineering. His recent works are concerned with off-the-cloud time-series prediction and demand forecast. He has collaborated with several tech startups in Tokyo.\n\nYoovraj Shinde is the Co-Founder of MLT and currently working as Technologist at Rakuten Institute of Technology. Electronics Engineer by heart, but worked in software industry for about 8 years. Worked on FrontEnd, BackEnd Java systems, iOS app development. His interests are Machine Learning and Robotics.\n\n\n## 9 WORKSHOP TEAMS: IMPRESSIONS\n\n\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/1.jpg\" width=\"600\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/2.jpg\" width=\"600\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/3.jpg\" width=\"600\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/4.jpg\" width=\"600\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/5.jpg\" width=\"600\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/6.jpg\" width=\"600\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Edge_AI/blob/master/images/7.jpg\" width=\"600\"\u003e\n\u003c/p\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachine-learning-tokyo%2Fedge_ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmachine-learning-tokyo%2Fedge_ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachine-learning-tokyo%2Fedge_ai/lists"}