Go Machine Learning Projects: Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
Xuanyi Chew
- 出版商: Packt Publishing
- 出版日期: 2018-11-30
- 售價: $1,520
- 貴賓價: 9.5 折 $1,444
- 語言: 英文
- 頁數: 348
- 裝訂: Paperback
- ISBN: 1788993403
- ISBN-13: 9781788993401
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相關分類:
Go 程式語言、Machine Learning
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相關翻譯:
Go語言機器學習實戰 (簡中版)
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相關主題
商品描述
Work through exciting projects to explore the capabilities of Go and Machine Learning
Key Features
- Explore ML tasks and Go's machine learning ecosystem
- Implement clustering, regression, classification, and neural networks with Go
- Get to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go
Book Description
Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured.
The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project.
By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.
What you will learn
- Set up a machine learning environment with Go libraries
- Use Gonum to perform regression and classification
- Explore time series models and decompose trends with Go libraries
- Clean up your Twitter timeline by clustering tweets
- Learn to use external services for your machine learning needs
- Recognize handwriting using neural networks and CNN with Gorgonia
- Implement facial recognition using GoCV and OpenCV
Who this book is for
If you're a machine learning engineer, data science professional, or Go programmer who wants to implement machine learning in your real-world projects and make smarter applications easily, this book is for you. Some coding experience in Golang and knowledge of basic machine learning concepts will help you in understanding the concepts covered in this book.
Table of Contents
- How to Solve All Machine Learning Problems
- Linear Regression - House Price Prediction
- Classification - Spam Email Detection
- Decomposing CO2 Trends Using Time Series Analysis
- Clean Up Your Personal Twitter Timeline by Clustering Tweets
- Neural Networks - MNIST Handwriting Recognition
- Convolutional Neural Networks - MNIST Handwriting Recognition
- Basic Facial Detection
- Hot Dog or Not Hot Dog - Using External Services
- What's Next?
商品描述(中文翻譯)
透過刺激的專案來探索 Go 和機器學習的能力
主要特點:
- 探索機器學習任務和 Go 的機器學習生態系統
- 使用 Go 實現聚類、回歸、分類和神經網絡
- 熟悉 Gorgonia、Gonum 和 GoCv 等庫,以在 Go 中訓練模型
書籍描述:
Go 是機器學習的完美語言;它有助於清晰描述複雜的算法,並且幫助開發人員理解如何運行高效優化的代碼。本書將教你如何在 Go 中實現機器學習,以便製作易於部署的程序,代碼不僅易於理解和調試,而且還能測量其性能。
本書首先引導你使用 Go 库和功能設置機器學習環境。然後,你將深入研究一個真實的房價數據集的回歸分析,並在 Go 中構建一個分類模型,將郵件分類為垃圾郵件或正常郵件。使用 Gonum、Gorgonia 和 STL,你將探索時間序列分析,並通過聚類推文來整理個人的 Twitter 時間軸。此外,你還將學習如何使用神經網絡和卷積神經網絡識別手寫字。最後,你將通過一個人臉檢測項目學習如何選擇最適合你的項目的機器學習算法。
通過閱讀本書,你將培養出堅實的機器學習思維,掌握強大的 Go 工具包,並對實際項目中機器學習算法的實現有深入的理解。
你將學到什麼:
- 使用 Go 库設置機器學習環境
- 使用 Gonum 執行回歸和分類
- 探索時間序列模型並使用 Go 库分解趨勢
- 通過聚類推文整理你的 Twitter 時間軸
- 學習使用外部服務滿足你的機器學習需求
- 使用 Gorgonia 進行手寫字識別和卷積神經網絡
- 使用 GoCV 和 OpenCV 實現人臉識別
本書適合對象:
如果你是機器學習工程師、數據科學專業人員或 Go 程序員,想在你的實際項目中實現機器學習並輕鬆製作智能應用,那麼本書適合你。具備 Golang 編程經驗和基本機器學習概念的知識將有助於你理解本書中涵蓋的概念。
目錄:
1. 如何解決所有機器學習問題
2. 線性回歸 - 房價預測
3. 分類 - 垃圾郵件檢測
4. 使用時間序列分析分解 CO2 趨勢
5. 通過聚類推文整理個人的 Twitter 時間軸
6. 神經網絡 - MNIST 手寫字識別
7. 卷積神經網絡 - MNIST 手寫字識別
8. 基本人臉檢測
9. 熱狗或非熱狗 - 使用外部服務
10. 下一步做什麼?