Machine Learning: A Quantitative Approach (dhl)

Henry H Liu

  • 出版商: CreateSpace Independent Publishing Platform
  • 出版日期: 2018-03-12
  • 售價: $1,855
  • 貴賓價: 9.8$1,818
  • 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)


更新日期:2018年4月24日:YOLOv3(You only look once)的示例 - 最先進的卷積神經網絡模型 - 已發布在書籍的下載網站www dot perfmath dot com上。還提供了如何獲取YOLO的調用圖以及如何使用macOS上的Instruments工具來理解YOLO的實現的指示。

 

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機器學習是一個新近復興的領域。它承諾將促進許多技術進步,可能會顯著提高我們生活的質量,從使用最新、流行、高檔的設備,如智能手機、家用設備、電視、遊戲機,甚至是自駕車等等,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中的人來說,它提供了更多的挑戰和機會。

 

無論您是一名修讀機器學習課程或目標是獲得機器學習學位的計算機科學學生,還是一名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而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。在整個文本中,您將獲得適當的文字解釋和圖形展示,不僅補充了相關的數學以保證其嚴謹性、簡潔性和必要性,還提供了高質量的示例,既包括傳統的機器學習模型,也包括深度學習模型。

 

本書鼓勵您在掌握各種機器學習模型的嚴謹、必要的數學基礎的同時,採取實踐方法。具體而言,本書幫助您:

  *了解機器學習可以解決哪些問題

  *了解各種機器學習模型,以及每個模型的優點和限制

  *了解各種主要機器學習算法在幕後的工作原理,以便能夠更有效地優化、調整和規模化各種模型

  *了解一些最先進的神經網絡架構,如卷積神經網絡(CNN)、循環神經網絡(RNN)和自編碼器(AE)等等

    

通過本書,您不僅將學習機器學習的工作原理,還將學習一些最流行的機器學習/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,在閱讀完本書後,您應該能夠立即開始進行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。