Hands-On Machine Learning with C++

Kolodiazhnyi, Kirill

  • 出版商: Packt Publishing
  • 出版日期: 2020-05-15
  • 售價: $2,110
  • 貴賓價: 9.5$2,005
  • 語言: 英文
  • 頁數: 530
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789955335
  • ISBN-13: 9781789955330
  • 相關分類: C++ 程式語言Machine Learning
  • 海外代購書籍(需單獨結帳)
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More Information
  • Explore how to load and preprocess various data types to suitable C++ data structures
  • Employ key machine learning algorithms with various C++ libraries
  • Understand the grid-search approach to find the best parameters for a machine learning model
  • Implement an algorithm for filtering anomalies in user data using Gaussian distribution
  • Improve collaborative filtering to deal with dynamic user preferences
  • Use C++ libraries and APIs to manage model structures and parameters
  • Implement a C++ program to solve image classification tasks with LeNet architecture

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.


This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format.


By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

  • Become familiar with data processing, performance measuring, and model selection using various C++ libraries
  • Implement practical machine learning and deep learning techniques to build smart models
  • Deploy machine learning models to work on mobile and embedded devices




  • 探索如何將各種數據類型加載並預處理為適合的C++數據結構

  • 使用各種C++庫應用關鍵機器學習算法

  • 了解網格搜索方法以找到機器學習模型的最佳參數

  • 使用高斯分佈實現用於過濾用戶數據異常的算法

  • 改進協同過濾以應對動態用戶偏好

  • 使用C++庫和API管理模型結構和參數

  • 使用LeNet架構實現C++程序來解決圖像分類任務




本書將讓您親自體驗調整和優化不同用例的模型,幫助您進行模型選擇和性能測量。您將學習到的技術包括產品推薦、集成學習和使用PyTorch C++ API、Caffe2、Shogun、Shark-ML、mlpack和dlib等現代C++庫進行異常檢測。接下來,您將通過圖像分類和情感分析等示例,探索神經網絡和深度學習,這將幫助您解決各種問題。然後,您將學習如何處理移動和雲平台上的生產和部署挑戰,並了解如何使用ONNX格式導出和導入模型。




  • 熟悉使用各種C++庫進行數據處理、性能測量和模型選擇

  • 實現實用的機器學習和深度學習技術,構建智能模型

  • 將機器學習模型部署到移動和嵌入式設備上


Kirill Kolodiazhnyi

Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience building machine learning models and data products using C++. He holds a bachelor degree in Computer Science from the Kharkiv National University of Radio-Electronics. He currently works in Kharkiv, Ukraine where he lives with his wife and daughter.


Kirill Kolodiazhnyi

Kirill Kolodiazhnyi 是一位經驗豐富的軟體工程師,擅長於客製化軟體開發。他擁有多年使用 C++ 建立機器學習模型和資料產品的經驗。他持有哈爾科夫國立無線電子大學的計算機科學學士學位。他目前在烏克蘭哈爾科夫工作,與妻子和女兒一起生活。