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Machine Learning Tokyo, the Digital Nature Group at the University of Tsukuba and ON-1 Tokyo are organizing a Deep Perception 2-day Hackathon to combine Deep Learning with interactive AR/VR/MR environments. The goal is to explore both fields and find novel ways of synthesis and interaction. We'll have teams of 3-5 people with different technical and … Continue reading DEEP PERCEPTION HACKATHON
150 people joined us for our Women in Machine Learning event on August 10 at Mercari. This event was organized in collaboration with Women Who Code Tokyo, a local chapter of the global nonprofit Women Who Code, an initiative that is supporting women to excel in tech careers. We heard two fantastic talks by Shreya … Continue reading WOMEN IN MACHINE LEARNING
Recently scientists at Elon Musk's Neuralink presented their impressive work on Brain-Machine-Interfaces. (Read the white paper) There are many labs and research groups all over the world working on Neural Interfaces. One of those labs is at UPCT Span. On July 31 we welcomed Antonio Lozano for a talk at ELSI. With a group of … Continue reading BRAIN-MACHINE INTERFACES, TALK & DISCUSSION
We finally kicked off our MLT Reinforcement Learning sessions with an "Intro to RL" study group at the Tokyo Metropolitan Library. Anugraha Sinha walked us through the following: Theory Introduction to RL Important elements of an RL problem Description of Markov Decision Process (MDP) and and Markov Assumption. Importance of parametrization of State, Action, Reward … Continue reading INTRO TO REINFORCEMENT LEARNING
Machine Learning Tokyo is a nonprofit organization dedicated to democratizing ML. We’re a core team of ML Engineers and Researchers and a community of 3,000 members in Japan. Our mission is to create, grow and sustain an inclusive, collaborative and engineering-focused environment. In the past we have organized more than 50 hands-on Machine Learning and Deep … Continue reading HOW TO ORGANIZE DEEP LEARNING WORKSHOPS
INTERACTIVE TOOLS Distill: Exploring Neural Networks with Activation Atlases Feature inversion to visualize millions of activations from an image classification network leads to an explorable activation atlas of features the network has learned. This can reveal how the network typically represents some concepts. A visual introduction to Machine Learning Available in many different … Continue reading INTERACTIVE TOOLS AND DL RESOURCES FOR BEGINNERS