
What we’re about
Machine Learning Tokyo (MLT) is an award-winning nonprofit organization 一般社団法人 based in Japan, operating globally and remotely. MLT is dedicated to democratizing Machine Learning through open education, open source and open science. We support a research- and engineering community of 10,000 members.
Open Education – MLT held more than 300 AI workshops, study sessions, talks and hackathons with thousands of participants in Tokyo and with remote participants from all over the world. Our events are inclusive and with an open education mindset, individuals can attend all events free of charge.
Open Source – Several volunteer teams within the MLT community are working on Machine Learning, Deep Learning, Reinforcement Learning and Robotics projects, including substantial work that has been done in the field of AI for Social Good. All projects are hosted on the public Machine Learning Tokyo GitHub Organization; code bases and repositories are published as open source projects.
Open Science – MLT teams have published research papers at international ML conference workshops and we’re continuously collaborating with Universities and Research Institutes in Japan to support open science and researchers with diverse academic backgrounds, including the University of Tokyo, Tokyo Institute of Technology and RIKEN CBS. We organized lectures, bootcamps and workshops on Machine Learning, Deep Learning and Data Science.
Find more information about MLT:
Website: https://www.mlt.ai/
Twitter: https://twitter.com/MLT
LinkedIn: https://www.linkedin.com/company/mltokyo/
● FIND MLT TALKS & VIDEOS ●
Youtube: https://www.youtube.com/MLTOKYO
● LOOKING FOR A NEW CAREER OPPORTUNITY? ●
Sign up and join the AI Career Network: https://forms.gle/KGz5P7JyhnVQCssv8
● CODE OF CONDUCT
MLT promotes an inclusive environment that values integrity, openness and respect. https://github.com/Machine-Learning-Tokyo/MLT_starterkit
● AI TOOLS WE LOVE & USE (AFFILIATE LINKS)
Upcoming events
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•OnlineBuilding Claude Skills: From One-Shot Agents to Context-Efficient Apps
OnlineAbstract
This session dives into how Claude Skills enable developers to turn prompt workflows into reusable, callable functions, bridging the gap between ad-hoc experimentation and production-grade AI systems. Jayson will share how he used Skills to build a LLM-powered Chrome extension in a single LLM interaction, and discuss how developers can design context-efficient, human-in-the-loop systems that remain useful and reliable as they grow in complexity.Bio
Jayson Cunanan is a Solutions Architect for Enterprise AI at Capgemini and the founder of JimakuAI, a Japan-based startup automating subtitle translation. He also mentors VibecodersPH, a developer community experimenting using vibe coding approach to learn fast, build real projects and land real work.Links
LinkedIn: https://www.linkedin.com/in/jsonmathsai
VibecodersPH: https://vibecoders.ph45 attendees
Past events
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