Machine Learners Archaeology of a Data Practice pdf下载

2018-06-25 09:36:44 141 阅读 AI 写同款 · GEO 优化AI 选题数据监测一键分发 ›

MATLAB机器学习实战：工业异常检测端到端流程机器学习在工业场景中的核心价值在于将原始信号转化为可判别特征，并构建鲁棒、可解释的诊断模型。其原理依赖于时频域联合分析、物理驱动的特征工程与小样本适配的模型选型。技术价值体现在无需第三方库即可完成特征提取、自动超参优化与可视化部署的闭环能力，显著提升数模竞赛与实时监测场景下的开发效率与结果可信度。典型应用场景包括振动信号故障识别、传感器数据异常预警及MATLAB原生GUI集成诊断系统。本文聚焦MATLAB Statistics and Machine Learning Toolbox与Signal Proc 阅读详情

If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?

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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