Machine Learning: A Quantitative Approach (dhl)
暫譯: 機器學習：量化方法

Henry H Liu

Machine Learning: A Quantitative Approach (dhl)
  • 出版商: CreateSpace Independent Publishing Platform
  • 出版日期: 2018-03-12
  • 售價: $1,855
  • 貴賓價: 9.9 折 $1,836
  • 語言: 英文
  • 頁數: 481
  • 裝訂: Paperback
  • ISBN: 1986487520
  • ISBN-13: 9781986487528
  • 相關分類: Machine Learning

    • 立即出貨(限量) (庫存=1)

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商品描述

Updated on 4/24/2018: Examples with YOLOv3 (You only look once) - the state of the art convolutional neural network models - posted to the book's download website at www dot perfmath dot com. Instructions are also given on how to obtain YOLO's call graph and understand YOLO's implementation with the Instruments tool on macOS.
 
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Machine learning is a newly-reinvigorated field. It promises to foster many technological advances that may improve the quality of our life significantly, from the use of latest, popular, high-gear gadgets such as smart phones, home devices, TVs, game consoles and even self-driving cars, and so on, to even more fun social and shopping experiences. Of course, for all of us in the circles of high education, academic research and various industrial fields, it offers more challenges and more opportunities. 

 
Whether you are a CS student taking a machine learning class or targeting a machine learning degree, or a scientist or an engineer entering the field of machine learning, this text helps you get up to speed with machine learning quickly and systematically. By adopting a quantitative approach, you will be able to grasp many of the machine learning core concepts, algorithms, models, methodologies, strategies and best practices within a minimal amount of time. Throughout the text, you will be provided with proper textual explanations and graphical exhibitions, augmented not only with relevant mathematics for its rigor, conciseness, and necessity but also with high quality examples for both conventional ML models and deep learning models. 
 
The text encourages you to take a hands-on approach while grasping all rigorous, necessary mathematical underpinnings behind various machine learning models. Specifically, this text helps you: 
  *Understand what problems machine learning can help solve 
  *Understand various machine learning models, with the strengths and limitations of each model 
  *Understand how various major machine learning algorithms work behind the scene so that you would be able to optimize, tune, and size various models more effectively and efficiently 
  *Understand a few state-of-the-art neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders (AEs), and so on 
    
From this book, you will not only learn how machine learning works but also learn some of the most popular machine learning/deep learning frameworks such as the sklearn, Caffe and Keras/TensorFlow for doing actual machine learning work. The author's goal is that after you are done with this text, you should be able to start embarking on various serious machine learning projects immediately, either using conventional machine learning models or state-of-the-art deep neural network models.

商品描述(中文翻譯)

**更新於 2018年4月24日**：包含 YOLOv3（You Only Look Once）範例 - 這是最先進的卷積神經網絡模型 - 已上傳至本書的下載網站 www.perfmath.com。還提供了如何獲取 YOLO 的調用圖以及如何使用 macOS 上的 Instruments 工具理解 YOLO 實現的說明。

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機器學習是一個重新振興的領域。它承諾促進許多技術進步，這些進步可能顯著改善我們的生活質量，從最新、流行的高端小工具，如智能手機、家庭設備、電視、遊戲主機，甚至自駕車等，到更有趣的社交和購物體驗。當然，對於我們這些在高等教育、學術研究和各種工業領域的人來說，它提供了更多的挑戰和機會。

無論您是正在修讀機器學習課程的計算機科學學生，還是目標獲得機器學習學位的學生，或是進入機器學習領域的科學家或工程師，本書都能幫助您快速且系統地掌握機器學習。通過採用定量方法，您將能夠在最短的時間內掌握許多機器學習的核心概念、算法、模型、方法論、策略和最佳實踐。在整本書中，您將獲得適當的文字解釋和圖形展示，不僅增強了相關數學的嚴謹性、簡潔性和必要性，還提供了高質量的範例，涵蓋傳統機器學習模型和深度學習模型。

本書鼓勵您在掌握各種機器學習模型背後的嚴謹數學基礎時，採取實踐的方法。具體來說，本書幫助您：

* 理解機器學習可以解決的問題
* 理解各種機器學習模型，以及每個模型的優勢和限制
* 理解各種主要機器學習算法的運作原理，以便能夠更有效率地優化、調整和配置各種模型
* 理解幾種最先進的神經網絡架構，如卷積神經網絡（CNNs）、遞歸神經網絡（RNNs）和自編碼器（AEs）等

通過本書，您不僅將學習機器學習的運作原理，還將學習一些最受歡迎的機器學習/深度學習框架，如 sklearn、Caffe 和 Keras/TensorFlow，以進行實際的機器學習工作。作者的目標是，在您完成本書後，應能立即開始各種嚴肅的機器學習項目，無論是使用傳統的機器學習模型還是最先進的深度神經網絡模型。

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