Distill series – diffusion model.

Distilling Neuro-Symbolic AI

Distilling Reinforcement Learning

The paper introduces a hierarchical reinforcement learning framework called Feudal Networks (FuN) that enables agents to learn a hierarchy of temporal abstractions. The proposed model consists of a Manager and a Worker, with the Manager learning high-level policies and the Worker learning low-level policies. The approach is demonstrated to achieve state-of-the-art performance on a range of challenging tasks in the Atari domain.

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Wikipedia

Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predcitors) in a learning problem. Maxumum-likelihood training can be done by evaluting a closed-form exporession, which takes linear time, rather tahn by expensive iterative approximation as used for many other typs of classifier. Wikipedia

If you ask yourself what’s the most important thing in machine learning, what’s your answer? All data scientist would have different answers.

supervised unsupervised feature label … 머신러닝을 시작하게 되면 새로 배워야 하는 용어들이 많죠? 하지만 이러한 용어들을 자신의 개념으로 잘 정리하는 것이 참 중요합니다. 왜냐하면, 우리가 앞으로 배우게 될 머신러닝의 기초가 되기 때문이죠…

여러분들의 데이터는 안전합니까?

To protect our system and computer we should make sure that data which we download is clean. Everytime we bring data to our system or user upload data such as file attachments, we must make sure that data is free from viruses and trojans.