AI, Machine Learning and Deep Learning this month, at a glance.
Probability Distributions, Probability and Stats, Bayesian Inference
Free annotation tools for Deep Learning, Computer Vision and Natural Language Processing tasks.
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)
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
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