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 6,000 members.

Open Education – MLT held more than 150 AI workshops, study sessions and talks 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:

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 (3)

Dive into Deep Learning: Coding Session #3– RNN model (APAC)

📌 Session #3 – RNN model (LSTM) implementation
📌 Introduction, Coding & Discussion

About:
The goal of this series is to provide code-focused sessions by reimplementing selected models from the interactive open source book "Dive into Deep Learning" http://d2l.ai/ by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. In this session, we will discuss Recurrent Neural networks and we’ll be covering parts of Chapter 8. Recurrent Neural networks. https://d2l.ai/chapter_recurrent-neural-networks/ and, Chapter 9. Modern Recurrent Neural Networks https://d2l.ai/chapter_recurrent-modern/. We recommend interested participants read these chapters of the book to take full advantage of the session.

These sessions are meant for people who are interested in implementing models from scratch and/or never have implemented a model before. We hope to help participants either get started in their Machine Learning journey or deepen their knowledge if they already have previous experience.

We will try to achieve this by:

* Helping participants to create their models end-to-end by reimplementing models from scratch, and discussing what modules/elements need to be included (e.g. data preprocessing, dataset generation, data transformation, etc…) to train an ML model.

* Discussing and resolving coding questions participants might have during the sessions.

📌 Session Leads: Mrityunjay Bhardwaj, and Pierre Wüthrich

📌 Prerequisites
Even though we welcome everybody to join the sessions, it is highly recommended to have at least intermediate Python skills as we will be using PyTorch to implement models. We also recommend participants have a foundational knowledge of calculus, linear algebra, and statistics/probability theory.

📌 Session Structure
● 30 min – Introduction
● 60 min – Live Coding
● 30 min – Discussion

📌 Join Zoom Meeting
https://us02web.zoom.us/j/84734768260?pwd=Y0N6THZCeVJScFZyN1lXKzR0WEcrQT09

📌 ORGANIZER BIO

Mrityunjay Bhardwaj is the Head of AI at Jupiter AI Labs which focuses on providing research-oriented enterprise-grade ML Solutions. Apart from that, he is also trying his hand in ML research. https://twitter.com/mrityunjay_99

Pierre Wüthrich is an AI Research Engineer at Elix Inc. focusing on drug discovery and material informatics. Before joining Elix, he gained experience in the field of applied reinforcement learning at another startup company specialized in machine automation through machine learning. https://www.linkedin.com/in/pierre-wuethrich/
https://twitter.com/pierre_wuethri

● FULL CURRICULUM
📌 Session 1:
Coding env setup example and book presentation
A quick review of ML domains (supervised/unsupervised/RL)
General Architecture/Components of ML code
Implementation of simple MLP-model
http://d2l.ai/chapter_introduction/index.html

📌 Session 2:
CNN model (LeNet/ResNet) implementation
http://d2l.ai/chapter_convolutional-neural-networks/index.html

📌 Session 3:
RNN model (LSTM) implementation
http://d2l.ai/chapter_recurrent-neural-networks/index.html

📌 Session 4:
Attention mechanism (Transformer) implementation
http://d2l.ai/chapter_attention-mechanisms/index.html

📌 Session 5:
Attention mechanism (Transformer) implementation
http://d2l.ai/chapter_attention-mechanisms/index.html

📌 Session 6:
Generative adversarial networks (DCGAN) implementation
http://d2l.ai/chapter_generative-adversarial-networks/index.html

● 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/3ai1kte

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

Dive into Deep Learning: Coding Session #3– RNN model (Americas/EMEA)

📌 Session #3 – RNN model (LSTM) implementation
📌 Introduction, Coding & Discussion

About:
The goal of this series is to provide code-focused sessions by reimplementing selected models from the interactive open source book "Dive into Deep Learning" http://d2l.ai/ by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. In this session, we will discuss Recurrent Neural networks and we’ll be covering parts of Chapter 8. Recurrent Neural networks. https://d2l.ai/chapter_recurrent-neural-networks/ and, Chapter 9. Modern Recurrent Neural Networks https://d2l.ai/chapter_recurrent-modern/. We recommend interested participants read these chapters of the book to take full advantage of the session.

These sessions are meant for people who are interested in implementing models from scratch and/or never have implemented a model before. We hope to help participants either get started in their Machine Learning journey or deepen their knowledge if they already have previous experience.

We will try to achieve this by:

* Helping participants to create their models end-to-end by reimplementing models from scratch, and discussing what modules/elements need to be included (e.g. data preprocessing, dataset generation, data transformation, etc…) to train an ML model.

* Discussing and resolving coding questions participants might have during the sessions.

📌 Session Leads: Devansh Agarwal and Kshitij Aggarwal

📌 Prerequisites
Even though we welcome everybody to join the sessions, it is highly recommended to have at least intermediate Python skills as we will be using PyTorch to implement models. We also recommend participants have a foundational knowledge of calculus, linear algebra, and statistics/probability theory.

📌 Session Structure
● 30 min – Introduction
● 60 min – Live Coding
● 30 min – Discussion

📌 Join Zoom Meeting
https://us02web.zoom.us/j/82258057264?pwd=dUVyWkQ3dElyNDR4TTM4L3BDNHVEdz09

📌 ORGANIZER BIO

Devansh Agarwal is a Data Scientist at BMS. He graduated with a Ph.D. in Astronomy where he has developed pipelines using high-performance computing and machine learning to aid the discovery of astronomical objects. https://www.linkedin.com/in/devanshkv/

Kshitij Aggarwal is a 4th-year graduate student at the Department of Physics and Astronomy at West Virginia University. He uses data analysis, machine learning, and high-performance computing to discover and study a new class of astronomical objects called Fast Radio Bursts. https://kshitijaggarwal.github.io/
https://www.linkedin.com/in/kshitijaggarwal13/

● FULL CURRICULUM
📌 Session 1:
Coding env setup example and book presentation
A quick review of ML domains (supervised/unsupervised/RL)
General Architecture/Components of ML code
Implementation of simple MLP-model
http://d2l.ai/chapter_introduction/index.html

📌 Session 2:
CNN model (LeNet/ResNet) implementation
http://d2l.ai/chapter_convolutional-neural-networks/index.html

📌 Session 3:
RNN model (LSTM) implementation
http://d2l.ai/chapter_recurrent-neural-networks/index.html

📌 Session 4:
Attention mechanism (Transformer) implementation
http://d2l.ai/chapter_attention-mechanisms/index.html

📌 Session 5:
Attention mechanism (Transformer) implementation
http://d2l.ai/chapter_attention-mechanisms/index.html

📌 Session 6:
Generative adversarial networks (DCGAN) implementation
http://d2l.ai/chapter_generative-adversarial-networks/index.html

● 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/3ai1kte

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

Machine Learning AMA with Laurence Moroney @Google I/O 2021

We are excited to welcome Google AI Lead Laurence Moroney for an AMA session during Google I/O 2021! In the first part of the session Laurence will summarize the top 10 I/O announcements for ML Developers, and afterwards we’ll jump right into your questions. You can ask your questions in advance here on dory https://bit.ly/2RJWYEM or join us for the live session on May 20.

Google I/O 2021 is held virtually (and free) on May 18-20, check it out here: https://events.google.com/io/

📌 AGENDA

● 1:00-1:05 pm Welcome and introduction
● 1:05-1:15 pm Top 10 I/O ML announcements, Laurence Moroney, AI Lead at Google
● 2:00 pm Closing

📌 SPEAKER BIO
Laurence leads the AI advocacy team at Google, where he's focussed on training and equipping millions of developers to be successful with using ML in Mobile, Web and Cloud Apps. He's the best-selling author of numerous books, including the recent "AI and Machine Learning for Coders" at O'Reilly. Laurence is based in Seattle, Washington.

https://twitter.com/lmoroney

● 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/3ai1kte

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

Past Events

MLT __init__ Session #5: RNN Encoder-Decoder

Online event

Photos (198)

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