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书名 | OpenCV机器学习(影印版)(英文版) |
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作者 | (美)迈克尔·贝耶勒 |
出版社 | 东南大学出版社 |
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简介 | 内容推荐 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。 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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