Automated Machine Learning: Methods, Systems, Challenges (Hardcover)

Hutter, Frank, Kotthoff, Lars, Vanschoren, Joaquin

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商品描述

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

 

商品描述(中文翻譯)

這本開放存取的書籍首次全面介紹了自動機器學習(AutoML)的一般方法,收集了基於這些方法的現有系統的描述,並討論了AutoML系統的首批國際挑戰。商業機器學習應用的最近成功和該領域的快速增長,創造了對可以輕鬆使用且無需專業知識的現成機器學習方法的高需求。然而,許多最近的機器學習成功關鍵地依賴於人類專家,他們手動選擇適當的機器學習架構(深度學習架構或更傳統的機器學習工作流程)及其超參數。為了克服這個問題,AutoML領域以優化和機器學習本身的原則為基礎,致力於逐步自動化機器學習。本書為研究人員和高級學生提供了進入這個快速發展領域的入門點,同時也為希望在工作中使用AutoML的從業人員提供了參考。