Experimentation for Engineers: From A/B Testing to Bayesian Optimization
            
暫譯: 工程師的實驗:從 A/B 測試到貝葉斯優化
        
        Sweet, David
- 出版商: Manning
- 出版日期: 2023-03-07
- 售價: $2,150
- 貴賓價: 9.5 折 $2,043
- 語言: 英文
- 頁數: 248
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617298158
- ISBN-13: 9781617298158
- 
    相關分類:
    
      Machine Learning
 
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商品描述
Learn practical and modern experimental methods used by engineers in technology and trading.
Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of methods for optimizing machine learning systems, quantitative trading strategies, and more. You'll start with a deep dive into A/B testing, and then graduate to advanced methods used to improve performance in highly competitive industries like finance and social media. The experimentation skills you'll master in this unique, practical guide will quickly reveal which approaches and features deliver real results for your business.
In Experimentation for Engineers, you'll learn how to evaluate the changes you make to your system and ensure that your experiments don't undermine revenue or other business metrics. By the time you're done, you'll be able to seamlessly deploy changes to production while avoiding common pitfalls.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
商品描述(中文翻譯)
學習工程師在技術和交易中使用的實用和現代實驗方法。
工程師的實驗:從 A/B 測試到貝葉斯優化 是一本針對優化機器學習系統、量化交易策略等的工具書。您將從深入了解 A/B 測試開始,然後進入用於提高金融和社交媒體等高度競爭行業性能的先進方法。您在這本獨特的實用指南中掌握的實驗技能,將迅速揭示哪些方法和特徵能為您的業務帶來實際成果。
在 工程師的實驗 中,您將學會如何評估對系統所做的更改,並確保您的實驗不會損害收入或其他業務指標。當您完成時,您將能夠無縫地將更改部署到生產環境中,同時避免常見的陷阱。
購買印刷版書籍可獲得 Manning Publications 提供的免費電子書,格式包括 PDF、Kindle 和 ePub。
作者簡介
David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram, where he used experimental methods to tune trading systems and recommender systems. This book is an extension of his lectures on tuning quantitative trading systems given at NYU Stern over the past three years.
作者簡介(中文翻譯)
大衛·斯威特曾在GETCO擔任量化交易員,並在Instagram擔任機器學習工程師,期間他使用實驗方法來調整交易系統和推薦系統。本書是他在過去三年於NYU Stern所講授的量化交易系統調整課程的延伸。
 
 
     
     
     
     
     
     
     
     
    