Machine Learning With Go

Daniel Whitenack

Machine Learning With Go

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商品描述

Key Features

  • Build simple, but powerful, machine learning applications that leverage Go’s standard library along with popular Go packages.
  • Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go
  • Understand when and how to integrate certain types of machine learning model in Go applications.

Book Description

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.

Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.

The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.

Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.

What you will learn

  • Learn about data gathering, organization, parsing, and cleaning.
  • Explore matrices, linear algebra, statistics, and probability.
  • See how to evaluate and validate models.
  • Look at regression, classification, clustering.
  • Learn about neural networks and deep learning
  • Utilize times series models and anomaly detection.
  • Get to grip with techniques for deploying and distributing analyses and models.
  • Optimize machine learning workflow techniques

About the Author

Daniel Whitenack (@dwhitena), PhD, is a trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (GopherCon, JuliaCon, PyCon, ODSC, Spark Summit, and more), teaches data science/engineering at Purdue University (@LifeAtPurdue), and, with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

Table of Contents

  1. Gathering and Organizing Data
  2. Matrices, Probability, and Statistics
  3. Evaluation and Validation
  4. Regression
  5. Classification
  6. Clustering
  7. Time Series and Anomaly Detection
  8. Neural Networks and “Deep” Learning
  9. Deploying and distributing Analyses and Models
  10. Appendix: Algorithms/Techniques Related to ML

商品描述(中文翻譯)

主要特點


  • 建立簡單但強大的機器學習應用程式，利用Go的標準庫以及流行的Go套件。

  • 學習在Go中成功實現機器學習所需的統計、算法和技術。

  • 了解何時以及如何在Go應用程式中整合特定類型的機器學習模型。

書籍描述

本書的目標是將讀者培養成為能夠利用Go建立堅固且有價值的應用程式的高效創新數據分析師。為此，本書清晰地介紹了在Go中建立預測模型的技術方面，同時也幫助讀者了解機器學習工作流程在實際場景中的應用。

《使用Go進行機器學習》向讀者展示如何在機器學習中保持高水準的生產力，同時也提供了克服在工程組織中整合機器學習時常遇到的挑戰的模式。

讀者將首先獲得如何從各種來源收集、組織和解析真實工作數據的扎實理解。然後，讀者將開發一個堅實的統計工具包，使他們能夠快速理解數據集的內容。最後，讀者將通過相關的Go套件實際操作實現基本的機器學習技術（回歸、分類、聚類等）。

最終，讀者將擁有堅實的機器學習思維和強大的Go技術工具包，包括技巧、套件和實例實現。

你將學到什麼


  • 了解數據收集、組織、解析和清理。

  • 探索矩陣、線性代數、統計和概率。

  • 了解如何評估和驗證模型。

  • 研究回歸、分類、聚類。

  • 學習神經網絡和深度學習。

  • 利用時間序列模型和異常檢測。

  • 掌握部署和分發分析和模型的技巧。

  • 優化機器學習工作流程技巧。

關於作者

Daniel Whitenack（@dwhitena）博士是一位受過訓練的數據科學家，目前在Pachyderm（@pachydermIO）工作。Daniel開發創新的分佈式數據管道，包括預測模型、數據可視化、統計分析等。他曾在世界各地的會議上發表演講（GopherCon、JuliaCon、PyCon、ODSC、Spark Summit等），在普渡大學（@LifeAtPurdue）教授數據科學/工程，並與Ardan Labs（@ardanlabs）一起維護Jupyter的Go核心，積極幫助組織對各種開源數據科學項目的貢獻。

目錄


  1. 數據收集和組織

  2. 矩陣、概率和統計

  3. 評估和驗證

  4. 回歸

  5. 分類

  6. 聚類

  7. 時間序列和異常檢測

  8. 神經網絡和「深度」學習

  9. 部署和分發分析和模型

  10. 附錄：與機器學習相關的算法/技術

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