Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
暫譯: 機器學習中的超參數優化：提升您的機器學習和深度學習模型效率

Agrawal, Tanay

Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
  • 出版商: Apress
  • 出版日期: 2020-11-29
  • 定價: $1,400
  • 售價: 9.5 折 $1,330
  • 貴賓價: 9.0 折 $1,260
  • 語言: 英文
  • 頁數: 164
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484265785
  • ISBN-13: 9781484265789
  • 相關分類: Machine Learning

    • 立即出貨 (庫存=1)

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商品描述

Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.

This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history.

The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries.

Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.

Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work.

 

What You Will Learn

 

  • Discover how changes in hyperparameters affect the model's performance.
  • Apply different hyperparameter tuning algorithms to data science problems
  • Work with Bayesian optimization methods to create efficient machine learning and deep learning models
  • Distribute hyperparameter optimization using a cluster of machines
  • Approach automated machine learning using hyperparameter optimization

 

Who This Book Is For

Professionals and students working with machine learning.

 

 

商品描述(中文翻譯)

深入探討機器學習模型的超參數調整，並專注於什麼是超參數以及它們如何運作。本書討論了從基礎到進階的不同超參數調整技術。

這是一本逐步指導超參數優化的指南，首先介紹什麼是超參數以及它們如何影響機器學習模型的不同方面。接著，將介紹一些基本的（暴力）超參數優化算法。此外，作者還針對時間和記憶體限制的問題，使用分散式優化方法。接下來，您將討論用於超參數搜尋的貝葉斯優化，該方法能從之前的歷史中學習。

本書討論了不同的框架，例如 Hyperopt 和 Optuna，這些框架實現了基於模型的全局優化（SMBO）算法。在這些討論中，您將專注於不同的方面，例如搜尋空間的創建和這些庫的分散式優化。

《機器學習中的超參數優化》幫助讀者理解這些算法的運作方式以及如何在現實數據科學問題中使用它們。最後一章總結了超參數優化在自動化機器學習中的角色，並以創建您自己的 AutoML 腳本的教程作結。

超參數優化是一項繁瑣的任務，因此請放鬆心情，讓這些算法為您工作。

您將學到的內容：

- 發現超參數的變化如何影響模型的性能。
- 將不同的超參數調整算法應用於數據科學問題。
- 使用貝葉斯優化方法創建高效的機器學習和深度學習模型。
- 使用一組機器進行超參數優化的分散式處理。
- 通過超參數優化接近自動化機器學習。

本書適合對象：

從事機器學習的專業人士和學生。

作者簡介

Tanay is a deep learning engineer and researcher, who graduated in 2019 in Bachelor of Technology from SMVDU, J&K. He is currently working at Curl Hg on SARA, an OCR platform. He is also advisor to Witooth Dental Services and Technologies. He started his career at MateLabs working on an AutoML Platform, Mateverse. He has worked extensively on hyperparameter optimization. He has also delivered talks on hyperparameter optimization at conferences including PyData, Delhi and PyCon, India.

作者簡介(中文翻譯)

Tanay 是一位深度學習工程師和研究員，於 2019 年從 J&K 的 SMVDU 獲得技術學士學位。他目前在 Curl Hg 工作，負責 SARA，一個光學字符識別（OCR）平台。他同時也是 Witooth 牙科服務與技術的顧問。他的職業生涯始於 MateLabs，參與開發自動機器學習平台 Mateverse。他在超參數優化方面有廣泛的經驗，並在包括 PyData Delhi 和 PyCon India 在內的會議上發表過有關超參數優化的演講。

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