Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost
Por um escritor misterioso
Last updated 28 março 2025
As a common and (sometimes) proven belief, deep learning systems seem to learn uninterpretable representations and are far from human understanding. Recently, some studies have highlighted the fact that this may not always be applicable, and some networks may be able to learn human-readable representations. Unfortunately, this ability could merely come from the fact that these networks are exposed to human-generated data. So, to demonstrate their ability to learn like humans (and not that they are simply memorizing human-created labels), it is necessary to test them without any label. Following this idea, the DeepMind and Google Brain teams, together with

Understanding AlphaZero: A Basic Chess Neural Network

AlphaZero - Wikipedia
The Basics of Neural Networks for Chess nerds (and anyone interested in AI), by Ryan Leong, Nov, 2023

Four Deep Learning papers from late 2021 that will have a significant impact on 2022, by Leonardo Tanzi

Acquisition of chess knowledge in AlphaZero

AlphaZero Chess: How It Works, What Sets It Apart, and What It Can Tell Us, by Maxim Khovanskiy

Acquisition of Chess Knowledge in AlphaZero

AlphaZero Chess: How It Works, What Sets It Apart, and What It Can Tell Us, by Maxim Khovanskiy

From Zero to Master in Hours: AlphaZero Accelerates Reinforcement Learning

Four Deep Learning papers from late 2021 that will have a significant impact on 2022, by Leonardo Tanzi

Acquisition of chess knowledge in AlphaZero

Move over AlphaGo: AlphaZero taught itself to play three different games
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