Machine Learning for OpenCV
暫譯: OpenCV 的機器學習

Michael Beyeler

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商品描述

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.

About This Book

  • Load, store, edit, and visualize data using OpenCV and Python
  • Grasp the fundamental concepts of classification, regression, and clustering
  • Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide
  • Evaluate, compare, and choose the right algorithm for any task

Who This Book Is For

This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.

What You Will Learn

  • Explore and make effective use of OpenCV's machine learning module
  • Learn deep learning for computer vision with Python
  • Master linear regression and regularization techniques
  • Classify objects such as flower species, handwritten digits, and pedestrians
  • Explore the effective use of support vector machines, boosted decision trees, and random forests
  • Get acquainted with neural networks and Deep Learning to address real-world problems
  • Discover hidden structures in your data using k-means clustering
  • Get to grips with data pre-processing and feature engineering

In Detail

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine

商品描述(中文翻譯)

擴展您的 OpenCV 知識,掌握機器學習的關鍵概念,使用這本實用的實作指南。

本書介紹


  • 使用 OpenCV 和 Python 載入、儲存、編輯和視覺化數據

  • 掌握分類、回歸和聚類的基本概念

  • 理解、執行並實驗機器學習技術,使用這本易於跟隨的指南

  • 評估、比較並選擇適合任何任務的算法

本書適合誰

本書針對已經熟悉 OpenCV 的 Python 程式設計師;本書將提供您建立自己的機器學習系統所需的工具和理解,針對實際的現實世界任務進行調整。

您將學到什麼


  • 探索並有效利用 OpenCV 的機器學習模組

  • 學習使用 Python 進行計算機視覺的深度學習

  • 掌握線性回歸和正則化技術

  • 對物體進行分類,例如花卉品種、手寫數字和行人

  • 探索支持向量機、提升決策樹和隨機森林的有效使用

  • 熟悉神經網絡和深度學習,以解決現實世界的問題

  • 使用 k-means 聚類發現數據中的隱藏結構

  • 掌握數據預處理和特徵工程

詳細內容

機器學習不再僅僅是一個流行詞,它無處不在:從保護您的電子郵件,到自動標記照片中的朋友,再到預測您喜歡的電影。計算機視覺是當今機器學習最令人興奮的應用領域之一。