Mastering .NET Machine Learning

Jamie Dixon

  • 出版商: Packt Publishing
  • 出版日期: 2016-03-29
  • 售價: $2,320
  • 貴賓價: 9.5$2,204
  • 語言: 英文
  • 頁數: 358
  • 裝訂: Paperback
  • ISBN: 1785888404
  • ISBN-13: 9781785888403
  • 相關分類: .NETMachine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

About This Book

  • Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0
  • Set up your business application to start using machine learning techniques
  • Familiarize the user with some of the more common .NET libraries for machine learning
  • Implement several common machine learning techniques
  • Evaluate, optimize and adjust machine learning models

Who This Book Is For

This book is targeted at .NET developers who want to build complex machine learning systems. Some basic understanding of data science is required.

What You Will Learn

  • Write your own machine learning applications and experiments using the latest .NET Framework, including .NET Core 1.0
  • Set up your business application to start using machine learning
  • Accurately predict the future of your data using simple, multiple, and logistic regressions
  • Discover hidden patterns using decision trees
  • Acquire, prepare, and combine datasets to drive insights
  • Optimize business throughput using Bayes Classifier
  • Discover (more) hidden patterns using k-NN and Naive Bayes
  • Discover (even more) hidden patterns using k-means and PCA
  • Use Neural Networks to improve business decision making while using the latest ASP.NET technologies

In Detail

.NET is one of the widely used platforms for developing applications. With the meteoric rise of machine learning, developers are now keen on finding out how to make their .NET applications smarter using machine learning.

Mastering .NET Machine Learning is packed with real-world examples to explain how to easily use machine learning techniques in your business applications. You will begin with an introduction to F# and prepare yourselves for machine learning using the .NET Framework. You will then learn how to write a simple linear regression model and, forming a base with the regression model, you will start using machine learning libraries available in .NET Framework such as Math.NET, numl, and Accord.NET with examples. Next, you are going to take a deep dive into obtaining, cleaning, and organizing your data. You will learn the implementation of k-means and PCA using Accord.NET and numl libraries. You will be using Neural Networks, AzureML, and Accord.NET to transform your application into a hybrid scientific application. You will also see how to deal with very large datasets using MBrace and deploy machine learning models to IoT devices so that the machine can learn and adapt on the fly.

商品描述(中文翻譯)

關於本書



  • 基於.NET Framework 4.6.1,包含ASP.NET Core 1.0的範例

  • 設定您的業務應用程式以開始使用機器學習技術

  • 熟悉一些常見的.NET機器學習庫

  • 實現幾種常見的機器學習技術

  • 評估、優化和調整機器學習模型

本書適合對象


本書針對希望建立複雜機器學習系統的.NET開發人員。需要一些基本的數據科學理解。

您將學到什麼



  • 使用最新的.NET Framework(包括.NET Core 1.0)編寫自己的機器學習應用程式和實驗

  • 設定您的業務應用程式以開始使用機器學習

  • 使用簡單、多元和邏輯回歸準確預測數據的未來

  • 使用決策樹發現隱藏的模式

  • 獲取、準備和結合數據集以獲得洞察力

  • 使用貝葉斯分類器優化業務通過量

  • 使用k-NN和Naive Bayes發現更多隱藏的模式

  • 使用k-means和PCA發現更多隱藏的模式

  • 使用神經網絡改進業務決策,同時使用最新的ASP.NET技術

詳細內容


.NET是開發應用程式的廣泛使用平台之一。隨著機器學習的迅猛發展,開發人員現在渴望了解如何使用機器學習使他們的.NET應用程式更智能。


《精通.NET機器學習》充滿了實際示例,解釋了如何在您的業務應用程式中輕鬆使用機器學習技術。您將從介紹F#並為使用.NET Framework進行機器學習做準備開始。然後,您將學習如何編寫一個簡單的線性回歸模型,並使用.NET Framework中的機器學習庫(如Math.NET、numl和Accord.NET)進行實例演示。接下來,您將深入研究獲取、清理和組織數據的過程。您將學習使用Accord.NET和numl庫實現k-means和PCA。您將使用神經網絡、AzureML和Accord.NET將應用程式轉變為混合科學應用程式。您還將了解如何使用MBrace處理非常大的數據集,並將機器學習模型部署到物聯網設備上,以便機器可以即時學習和適應。