Skip to content

MLT | MACHINE LEARNING TOKYO

ABOUT
- MLT
- PARTNERS
- MEDIA
SUBSCRIBE

February 21, 2020 MACHINE LEARNING TOKYO

crop-286-215-3461-2596-0-img_7403.jpg

Click to share on Twitter (Opens in new window)

Leave a comment

Post navigation

Published Incrop-286-215-3461-2596-0-img_7403.jpg

Leave a Reply Cancel reply

Support MLT on Patreon

Become a Patron!

Search for:

FOLLOW MLT

Facebook
Twitter
GitHub
YouTube
Meetup
LinkedIn

CNN ARCHITECTURES

MATH REVIEW: PCA

ML NEWS

Upcoming Events

A rigorous introduction to probability theory: Lecture 1 January 25, 2022 at 11:15 am – 12:15 pm Online event We're excited to host a short course of 4 lectures on probability theory: These lectures by Michal Fabinger introduce basic concepts of probability theory in an intuitive yet rigorous way. This material should later help the participants understand scientific articles that use probability theory and statistics. Such knowledge is useful both formachine learning and data…
MLT __init__ Session #13: Multitask Prompted Training Enables Zero-Shot.. January 26, 2022 at 2:00 pm – 3:00 pm Online event 📌 Paper Reading & Discussion📌 Victor Sanh, Albert Webson, Colin Raffel, et al. (2021): Multitask Prompted Training Enables Zero-Shot Task Generalizationhttps://arxiv.org/abs/2110.08207 👉 SESSION11:00 - 11:05 pm Introduction11:05 - 11:30 pm Presentation11:30 - 12:00 am Discussion 👉 PRESENTER BIOVictor is a research scientist at Hugging Face. His work focuses on machine learning and natural language processing…
A rigorous introduction to probability theory: Lecture 2 February 1, 2022 at 11:15 am – 12:15 pm Online event We're excited to host a short course of 4 lectures on probability theory: These lectures by Michal Fabinger introduce basic concepts of probability theory in an intuitive yet rigorous way. This material should later help the participants understand scientific articles that use probability theory and statistics. Such knowledge is useful both formachine learning and data…

Top Posts & Pages

10 COOL GPT-3 DEMOS
CNN ARCHITECTURES: XCEPTION
Google's Teachable Machine
CNN ARCHITECTURES: DENSENET

Powered by WordPress.com.