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  • OPENCV机器学习(影印版) MichaelBeyeler 著 专业科技 文轩网
  • 新华书店正版
    • 作者: MichaelBeyeler著
    • 出版社: 东南大学出版社
    • 出版时间:2019-04-01 00:00:00
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    商品参数
    • 作者: MichaelBeyeler著
    • 出版社:东南大学出版社
    • 出版时间:2019-04-01 00:00:00
    • 版次:1
    • 印次:1
    • 字数:465千字
    • 页数:357
    • 开本:其他
    • 装帧:平装
    • ISBN:9787564183240
    • 国别/地区:中国
    • 版权提供:东南大学出版社

    OPENCV机器学习(影印版)

    作  者:MichaelBeyeler 著
    定  价:96
    出 版 社:东南大学出版社
    出版日期:2019年04月01日
    页  数:357
    装  帧:平装
    ISBN:9787564183240
    主编推荐

    内容简介

    本书首先介绍了统计学习的基本概念,例如分类和回归。介绍完所有的基础知识之后,就开始探究如决策树、支持向量机、贝叶斯网络等算法,学习如何将它们与其他OpenCV功能综合运用。你的机器学习技能会随着书中内容的进度一同提高,直到准备好学习当前最热门的主题:深度学习。在本书的结尾,你可以根据现有的源代码构建或是从头开发自己的算法来解决自己碰到的机器学习问题!

    作者简介

    迈克尔?贝耶勒是华盛顿大学神经工程和数据科学专业的博士后,主攻仿生视觉计算模型,用以为盲人植入人工视网膜(仿生眼睛),改善盲人的视觉体验。他的工作属于神经科学、计算机工程、计算机视觉和机器学习的交叉领域。他也是2015年Packt出版的《OpenCV with Python Blueprints》一书的作者,该书是构建高级计算机视觉项目的实用指南。同时他也是多个开源项目的积极贡献者,具有Python、C/C++、CUDA、MATLAB和Android的专业编程经验。他还拥有加利福尼亚大学欧文分校计算机科学专业的博士学位、瑞士苏黎世联邦理工学院生物医学专业的硕士学位和电子工程专业的学士学位。当他不“呆头呆脑”地研究大脑时,他会攀登雪山、参加现场音乐会或者弹钢琴。

    精彩内容

    目录
    Preface
    Chapter 1:A Taste of Machine Learning
    Getting started with machine learning
    Problems that machine learning can solve
    Getting started with Python
    Getting started with OpenCV
    Installation
    Getting the latest code for this book
    Getting to grips with Python's Anaconda distribution
    Installing OpenCV in a conda environment
    Verifying the installation
    Getting a glimpse of OpenCV's ML module
    Summary
    Chapter 2: Working with Data in OpenCV and Python
    Understanding the machine learning workflow
    Dealing with data using OpenCV and Python
    Starting a new IPython or Jupyter session
    Dealing with data using Python's NumPy package
    Importing NumPy
    Understanding NumPy arrays
    Accessing single array elements by indexing
    Creating multidimensional arrays
    Loading external datasets in Python
    Visualizing the data using Matplotlib
    Importing Matplotlib
    Producing a simple plot
    Visualizing data from an external dataset
    Dealing with data using OpenCV's TrainData container in C++
    Summary
    Chapter 3: First Steps in Supervised Learning
    Understanding supervised learning
    Having a look at supervised learning in OpenCV
    Measuring model performance with scoring functions
    Scoring classifiers using accuracy, precision, and recall
    Scoring regressors using mean squared error, explained variance, and R squared
    Using classification models to predict class labels
    Understanding the k-NN algorithm
    Implementing k-NN in OpenCV
    Generating the training data
    Training the classifier
    Predicting the label of a new data point
    Using regression models to predict continuous outcomes
    Understanding linear regression
    Using linear regression to predict Boston housing prices
    Loading the dataset
    Training the model
    Testing the model
    Applying Lasso and ridge regression
    Classifying iris species using logistic regression
    Understanding logistic regression
    Loading the training data
    Making it a binary classification problem
    Inspecting the data
    Splitting the data into training and test sets
    Training the classifier
    Testing the classifier
    Summary
    Chapter 4: Representing Data and Engineering Features
    Understanding feature engineering
    Preprocessing data
    Standardizing features
    Normalizing features
    Scaling features to a range
    Binarizing features
    Handling the missing data
    Understanding dimensionality reduction
    Implementing Principal Component Analysis (PCA) in OpenCV
    Implementing Independent Component Analysis (ICA)
    Implementing Non-negative Matrix Factorization (NMF)
    Representing categorical variables
    Representing text features
    Representing images
    Using color spaces
    Encoding images in RGB space
    Encoding images in HSV and HLS space
    Detecting corners in images
    Chapter 5: Using Decision Trees to Make a Medical Diagnosis
    Chapter 6: Detecting Pedestrians with Support Vector Machines
    Chapter 7: Implementing a Spam Filter with Bayesian Learning
    Chapter 8: Discovering Hidden Structures with Unsupervised Learning
    Chapter 9: Using Deed Learning to Classifv Handwritten Diqits
    Chapter 10: Combining Different Algorithms into an Ensemble
    Chapter 11:Selecting the Right Model with Hyperparameter Tuning
    Chapter 12: Wrapping Up

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