Machine Learners 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Machine Learners

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Adrian Mackenzie 作者
MIT Press
譯者
2018-11 出版日期
272 頁數
0 價格
叢書系列
9780262342544 圖書編碼

Machine Learners 在線電子書 圖書標籤: 信息史  科學史  數據  媒介考古學   


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Machine Learners 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Machine Learners 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Machine Learners 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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A clear critical reading of machine learning algorithm

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A clear critical reading of machine learning algorithm

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A clear critical reading of machine learning algorithm

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A clear critical reading of machine learning algorithm

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A clear critical reading of machine learning algorithm

Machine Learners 在線電子書 著者簡介

Adrian Mackenzie is Professor of Technological Cultures in the Department of Sociology at Lancaster University and the author of Wirelessness: Radical Empiricism in Network Cultures (MIT Press).


Machine Learners 在線電子書 著者簡介


Machine Learners 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Machine Learners 在線電子書 圖書描述

Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.

Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures.

Mackenzie’s account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.

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