Kwangjin Oh

[Jeremy Howard @jeremyphoward]

There's a lot of folks under the misunderstanding that it's now possible to run a 30B param LLM in <6GB, based on this GitHub discussion.

This is not the case. Understanding why gives us a chance to learn a lot of interesting stuff! 🧵
https://t.co/9xOfNyLUFr

[Sebastian Raschka @rasbt]

Large Language Models 3.0

In the new issue of Ahead of AI, I am discussing what's next for LLMs and developments centered around parameter efficiency and multimodality.

It's also been a particularly strong month for open-source AI!

🔗 https://t.co/jU8CXn6vUJ

[ML Review @ml_review]

"Build an AI to play Dino Run"

A tutorial to build a Reinforcement Learning model by @hellopaperspace

https://t.co/TSSYTUsfhL

[KDnuggets @kdnuggets]

Understanding LSTM and its diagrams #NLProc https://t.co/UDvi9biM2h

[ML Review @ml_review]

iVQA: Inverse Visual Question Answering #CVPR2018
By Feng Liu et al.

Generate a question that corresponds to a given image and answer pair.

https://t.co/gZi6A2hd05

[KDnuggets @kdnuggets]

How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1 https://t.co/kQH4glBjIP

[KDnuggets @kdnuggets]

What is the Difference Between Deep Learning and “Regular” Machine Learning? https://t.co/Vx4038xU2h

[Eric Jang @ericjang11]

Re-parameterize all the things! If you want to backprop thru samples from mixture, truncated, Gamma, Beta, Dirichlet, Student-t, or von Mises distributions, this paper has gotchu covered. Also faster than RSVI, another general reparameterization trick based on rejection sampling https://t.co/XZ97q21i6C

[ML Review @ml_review]

Monte Carlo Tree Search – Beginners Guide

https://t.co/Mhvsew2IGg #MachineLearning

[Ian Goodfellow @goodfellow_ian]

Self-attention for GANs. No more problems with losing track of how many faces the generator has drawn on the dog. https://t.co/WOWglD3ft5

[KDnuggets @kdnuggets]

Working With Numpy Matrices: A Handy First Reference https://t.co/lb6ahovsxv

[Michael Figurnov @mfigurnov]

Implicit Reparameterization Gradients by myself, @shakir_za and Andriy Mnih: https://t.co/z4drxddybg. An extension of reparameterization for many distributions, including Gamma, Beta, Dirichlet and von Mises, that is faster and easier to use than the previous methods.

[David Amador 🐙 @DJ_Link]

This website is amazing, linear algebra with interactive examples. Vectors, matrix, dot product, etc, cool resource for learning https://t.co/yH7Iw6ty1H

[당아 @dangboon13]

엄마가 홈플러스에서 당첨되어서 들고온 핸드선풍기인데ㅋㅋㅋㅋㅋㅋㅋ 아침에 내가 건전지 꽂아서 돌렸더니 이런 ㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋ
이거 어떻게 들고 다니라고 ㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋ 오늘 하루의 웃음지뢰가 되어버렸닼ㅋㅋㅋㅋㅋㅋ