Hands-On Machine Learning with C++
暫譯: C++ 實戰機器學習

Kolodiazhnyi, Kirill

Hands-On Machine Learning with C++

買這商品的人也買了...

商品描述

More Information
Learn
  • 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
About

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.
Features
  • 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

商品描述(中文翻譯)

更多資訊


學習內容


  • 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++ 庫應用關鍵的機器學習算法

  • 理解網格搜索方法以尋找機器學習模型的最佳參數

  • 實現一種使用高斯分佈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

  • 實現一個 C++ 程序來解決使用 LeNet 架構的圖像分類任務





關於本書

C++ 可以使您的機器學習模型運行得更快、更高效。本指南將幫助您學習機器學習 (ML) 的基本原理，展示如何使用 C++ 庫充分利用您的數據。本書以示例為基礎的方法，使初學者能夠輕鬆學習 C++ 的機器學習，並通過實際案例演示如何實現監督式和非監督式的 ML 算法。

 

本書將讓您親自調整和優化模型以適應不同的使用案例，協助您進行模型選擇和性能測量。您將涵蓋產品推薦、集成學習和異常檢測等技術，使用現代 C++ 庫，如 PyTorch C++ API、Caffe2、Shogun、Shark-ML、mlpack 和 dlib。接下來，您將探索神經網絡和深度學習，使用圖像分類和情感分析等示例，幫助您解決各種問題。之後，您將學習如何處理移動和雲平台上的生產和部署挑戰，然後發現如何使用 ONNX 格式導出和導入模型。

 

到本 C++ 書籍結束時，您將擁有實際的機器學習和 C++ 知識，以及使用 C++ 構建強大 ML 系統的技能。





特色


  • 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以構建智能模型

  • 將機器學習模型部署到移動和嵌入式設備上




作者簡介

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.

作者簡介(中文翻譯)

基里爾·科洛迪亞茲尼

基里爾·科洛迪亞茲尼是一位經驗豐富的軟體工程師，專精於客製化軟體開發。他擁有數年的經驗，使用 C++ 建立機器學習模型和數據產品。他擁有哈爾科夫國立無線電子大學的計算機科學學士學位。目前，他在烏克蘭哈爾科夫工作，與妻子和女兒一起生活。

類似商品