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
Tokyo
MLT @JUNCTION TOKYO
Here's a quick recap of the two projects we built at Junction Tokyo. MLT x 2020 We wanted to build a Deep Learning system that serves the Tokyo Olympics 2020 and help facilitate a safe, fun and incredible experience for all people involved - for participants from Japan and all over the world and … Continue reading MLT @JUNCTION TOKYO
WORKSHOP: GANs
Machine Learning Tokyo is organizing a hands-on workshop on GANs (Generative Adversarial Networks) for the Deep Learning community in Tokyo. It will be a dynamic and interactive working session, with 3 essential parts: - Part 1: Introduction to GANs - Part 2: Technical Implementation - Part 3: Tips & Tricks: Hyperparameter Tuning - - … Continue reading WORKSHOP: GANs
EVENT: DEEP LEARNING FOR NLP
Machine Learning Tokyo is co-hosting an event on April 3 for those, who are interested in Deep Learning and Natural Language Processing. We are excited to welcome two experts in the field, coming all the way from Tel Aviv, Israel and Melbourne, Australia. Deep Learning for NLP First, Sam Davis (CEO amplified.ai) kicks off the … Continue reading EVENT: DEEP LEARNING FOR NLP
ML & DL EVENTS OF THE WEEK
Tuesday, December 12, 7-9 pm, Shibuya Data Pre-Processing Study Group (データ前処理研究会) It's a pain. Data pre-processing. Therefore, AI Team is hosting a study group, providing online materials and the opportunity to learn from experts. [meetup] Friday, December 15, 7-9:30 pm, Roppongi An invitation to Deep Learning with applications to textual data Pontus Stenetorp from … Continue reading ML & DL EVENTS OF THE WEEK
Rule #1: Know your data
We've been talking a lot about what it takes to build a truly sophisticated model and get the best results. We looked at the Titanic challenge on Kaggle, but whatever we did, the accuracy did not exceed a certain percentage. So what to do? Whatever problem you're dealing with, having domain expertise gives you a … Continue reading Rule #1: Know your data
ML TOKYO: IDEATHON
Hi all! Our ideathon is coming up! Here are some resources that we can use for our brainstorming-session on October 7: CHEATSHEETS Python for Data Science Pandas NumPy Scikit-Learn Keras OPEN DATA Kaggle Open ML (19629 datasets) 日本のオープンデータ (Japanese open data) Data.world (US) UCI Machine Learning Repository (394 datasets, classics in ML) API, … Continue reading ML TOKYO: IDEATHON
