What's the most important implementation information in a Deep Learning paper and how do we code it up? MLT Director Dimitris Katsios shows you exactly that with a series on CNN Architectures including notebooks, visualizations and videos. To kick off the series, Dimitris picked some of the earliest Convolutional Neural Network papers. This series will … Continue reading CNN ARCHITECTURES (1-5)
Deep Learning
5,000 MLT MEMBERS
Amidst the coronavirus pandemic we want to share some wonderful news. We are extremely happy that Machine Learning Tokyo doubled in size in this past year from 2,400 members in March 2019 to more than 5,000 members in March 2020. Going online and opening up to the global AI community made MLT more diverse, bigger … Continue reading 5,000 MLT MEMBERS
NEW MEMBERS ON THE MLT BOARD OF DIRECTORS
We are honored and excited to welcome Dimitris Katsios and Alisher Abdulkhaev to the MLT Board of Directors. Both have been part of our Computer Vision Team since 2018, they have been tirelessly supporting the community, teaching CNN architectures in numerous workshops in Tokyo and have always been an essential part of the organization. They … Continue reading NEW MEMBERS ON THE MLT BOARD OF DIRECTORS
GENERATIVE DEEP LEARNING (READING & DISCUSSION)
Our Generative Deep Learning Sessions are dedicated to reading and discussing the book "Generative Deep Learning" by David Foster (O'Reilly). The first sessions will be held on Sunday, March 1, remaining sessions will be held weekly on Saturdays from 3-5PM (JST). All sessions are remote and accessible via zoom. Session structure ● 1.5 silent reading … Continue reading GENERATIVE DEEP LEARNING (READING & DISCUSSION)
AI CURRICULUM
We created a small repository linking to open Deep Learning and Reinforcement Learning lectures provided by MIT, Stanford University and UC Berkeley. ⚪ MIT 6.S191: Introduction to Deep Learning | 2020⚪ CS231n: CNNs for Visual Recognition, Stanford | Spring 2019⚪ CS224n: NLP with Deep Learning, Stanford | Winter 2019⚪ CS285: Deep Reinforcement Learning, UC Berkeley | … Continue reading AI CURRICULUM
ML TOKYO TALKS: QUANTUM EDITION
Spaces Shinagawa was packed at our ML TOKYO TALKS event – Quantum Computing/Quantum Machine Learning edition in collaboration with the Association of Italian Researchers in Japan (AIRJ). We were fortunate to welcome two experts in the field of Quantum Computing: Mattia Fiorentini (Head of Machine Learning and Quantum Algorithms at Cambridge Quantum Computing) and Nathan … Continue reading ML TOKYO TALKS: QUANTUM EDITION