What we're about

Machine Learning Tokyo (MLT) is an award-winning nonprofit organization 一般社団法人 based in Japan. MLT is dedicated to democratizing Machine Learning through Open Education, Open Source and Open Science. We support a research- and engineering community of 4,500 members.

Open Education – MLT held more than 70 AI workshops, study sessions and talks with more than 5,000 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:

Meetup: https://www.meetup.com/Machine-Learning-Tokyo/

Twitter: https://twitter.com/__MLT__

LinkedIn: https://www.linkedin.com/company/mltokyo/

Facebook: https://www.facebook.com/machinelearningtokyo/

MLT Blog: http://machinelearningtokyo.com/

● MLT PATRON ●
Become a MLT Patron and help us to keep MLT meetups like this inclusive and for free. https://www.patreon.com/MLTOKYO

● SUBSCRIBE ●
Subscribe to our monthly newsletter: https://mltokyo.ai/membership-join

● FIND MLT RESOURCES ●
Github: https://github.com/Machine-Learning-Tokyo
Youtube: https://www.youtube.com/MLTOKYO

Join us on Slack: https://bit.ly/2Yb0uXI

● RECRUITING ●
MLT events are for community building and knowledge sharing. We politely ask that company representatives, recruiters and consultants looking to hire or sell their services do not participate in MLT activities or approach members in any form.

● CODE OF CONDUCT

MLT promotes an inclusive environment that values integrity, openness and respect. https://github.com/Machine-Learning-Tokyo/MLT_starterkit

Upcoming events (1)

Review/Discussion: Part II Central Machine Learning Problems

We are slowly approaching the end of our ML Math Reading Sessions – congrats to all participants for getting this far! This particular meetup is a remote review session of Part II: Central Machine Learning Problems. We'll discuss some interesting parts from the past chapters and answer open questions. 📌 We'll also have the following mini-presentations: ● Fourier transforms and a brief comparison with SVD by Jayson Cunanan, Ph.D. https://www.linkedin.com/in/jayson-cunanan-phd/ ● Principal Component Analysis by Hiroshi Urata, Data Scientist https://www.linkedin.com/in/hiroshi-u/ 📌 Join Zoom Meeting: https://zoom.us/j/676060852 📌 IMPORTANT INFO: The session will be recorded. 📚This session is hosted by Emil Vatai. Please refer to the MML Book “Mathematics For Machine Learning” by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, to be published by Cambridge University Press. https://mml-book.github.io/ You can find more information about the book, the sessions and how to join the communication channels here https://machinelearningtokyo.com/2019/11/28/ml-math-reading-sessions/ The goal is to be more disciplined and create a new collaborative and interactive way of studying. More than 800 people from all over the world expressed their interest to be part of this, and luckily, we have found a great international leadership team that will lead the sessions in different time zones from January. 📌 Table of Contents ● Part I: Mathematical Foundations - Introduction and Motivation - Linear Algebra - Analytic Geometry - Matrix Decompositions - Vector Calculus - Probability and Distribution - Continuous Optimization ● Part II: Central Machine Learning Problems - When Models Meet Data - Linear Regression - Dimensionality Reduction with Principal Component Analysis - Density Estimation with Gaussian Mixture Models - Classification with Support Vector Machines ● MLT PATRON Become a MLT Patron and help us to keep MLT meetups like this inclusive and for free. https://www.patreon.com/MLTOKYO ● SUBSCRIBE Subscribe to our monthly newsletter: https://mltokyo.ai/membership-join ● FIND MLT RESOURCES Github: https://github.com/Machine-Learning-Tokyo Youtube: https://www.youtube.com/MLTOKYO Slack: https://bit.ly/2Yb0uXI ● RECRUITING MLT events are for community building and knowledge sharing. We politely ask that company representatives, recruiters and consultants looking to hire or sell their services do not participate in MLT activities or approach members in any form. ● CODE OF CONDUCT MLT promotes an inclusive environment that values integrity, openness and respect. https://github.com/Machine-Learning-Tokyo/MLT_starterkit

Past events (113)

ML Math Reading Session #9 (Americas, EMEA)

Online event

Photos (162)

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