基本信息
源码名称：使用opencv和python进行智能图像处理源代码完整版.zip
源码大小：69.13M
文件格式：.zip
开发语言：Python
更新时间：2023-07-05
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源码介绍
MATLAB与机器学习实例源代码完整版，一键运行，快速入门
文件清单
└── 使用opencv和python进行智能图像处理源代码
├── environment.yml
├── LICENSE
├── notebooks
│ ├── 00.00-Preface.ipynb
│ ├── 00.01-Foreword-by-Ariel-Rokem.ipynb
│ ├── 01.00-A-Taste-of-Machine-Learning.ipynb
│ ├── 02.00-Working-with-Data-in-OpenCV.ipynb
│ ├── 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb
│ ├── 02.02-Loading-External-Datasets-in-Python.ipynb
│ ├── 02.03-Visualizing-Data-Using-Matplotlib.ipynb
│ ├── 02.04-Visualizing-Data-from-an-External-Dataset.ipynb
│ ├── 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C .ipynb
│ ├── 03.00-First-Steps-in-Supervised-Learning.ipynb
│ ├── 03.01-Measuring-Model-Performance-with-Scoring-Functions.ipynb
│ ├── 03.02-Understanding-the-k-NN-Algorithm.ipynb
│ ├── 03.03-Using-Regression-Models-to-Predict-Continuous-Outcomes.ipynb
│ ├── 03.04-Applying-Lasso-and-Ridge-Regression.ipynb
│ ├── 03.05-Classifying-Iris-Species-Using-Logistic-Regression.ipynb
│ ├── 04.00-Representing-Data-and-Engineering-Features.ipynb
│ ├── 04.01-Preprocessing-Data.ipynb
│ ├── 04.02-Reducing-the-Dimensionality-of-the-Data.ipynb
│ ├── 04.03-Representing-Categorical-Variables.ipynb
│ ├── 04.04-Represening-Text-Features.ipynb
│ ├── 04.05-Representing-Images.ipynb
│ ├── 05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb
│ ├── 05.01-Building-Our-First-Decision-Tree.ipynb
│ ├── 05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb
│ ├── 05.03-Using-Decision-Trees-for-Regression.ipynb
│ ├── 06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb
│ ├── 06.01-Implementing-Your-First-Support-Vector-Machine.ipynb
│ ├── 06.02-Detecting-Pedestrians-in-the-Wild.ipynb
│ ├── 06.03-Additional-SVM-Exercises.ipynb
│ ├── 07.00-Implementing-a-Spam-Filter-with-Bayesian-Learning.ipynb
│ ├── 07.01-Implementing-Our-First-Bayesian-Classifier.ipynb
│ ├── 07.02-Classifying-Emails-Using-Naive-Bayes.ipynb
│ ├── 08.00-Discovering-Hidden-Structures-with-Unsupervised-Learning.ipynb
│ ├── 08.01-Understanding-k-Means-Clustering.ipynb
│ ├── 08.02-Compressing-Color-Images-Using-k-Means.ipynb
│ ├── 08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb
│ ├── 08.04-Implementing-Agglomerative-Hierarchical-Clustering.ipynb
│ ├── 09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb
│ ├── 09.01-Understanding-perceptrons.ipynb
│ ├── 09.02-Implementing-a-Multi-Layer-Perceptron-in-OpenCV.ipynb
│ ├── 09.03-Getting-Acquainted-with-Deep-Learning.ipynb
│ ├── 09.04-Training-an-MLP-in-OpenCV-to-Classify-Handwritten-Digits.ipynb
│ ├── 09.05-Training-a-Deep-Neural-Net-to-Classify-Handwritten-Digits-Using-Keras.ipynb
│ ├── 10.00-Combining-Different-Algorithms-Into-an-Ensemble.ipynb
│ ├── 10.01-Understanding-Ensemble-Methods.ipynb
│ ├── 10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb
│ ├── 10.03-Using-Random-Forests-for-Face-Recognition.ipynb
│ ├── 10.04-Implementing-AdaBoost.ipynb
│ ├── 10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb
│ ├── 11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb
│ ├── 11.01-Evaluating-a-Model.ipynb
│ ├── 11.02-Understanding-Cross-Validation-Bootstrapping-and-McNemar's-Test.ipynb
│ ├── 11.03-Tuning-Hyperparameters-with-Grid-Search.ipynb
│ ├── 11.04-Chaining-Algorithms-Together-to-Form-a-Pipeline.ipynb
│ ├── 12.00-Wrapping-Up.ipynb
│ ├── data
│ │ ├── chapter6
│ │ │ ├── pedestrians128x64.tar.gz
│ │ │ ├── pedestrians_neg.tar.gz
│ │ │ └── pedestrian_test.jpg
│ │ ├── chapter7
│ │ │ ├── beck-s.tar.gz
│ │ │ ├── BG.tar.gz
│ │ │ ├── farmer-d.tar.gz
│ │ │ ├── GP.tar.gz
│ │ │ ├── kaminski-v.tar.gz
│ │ │ ├── kitchen-l.tar.gz
│ │ │ ├── lokay-m.tar.gz
│ │ │ ├── SH.tar.gz
│ │ │ └── williams-w3.tar.gz
│ │ ├── cover.jpg
│ │ ├── haarcascade_frontalface_default.xml
│ │ └── lena.jpg
│ └── figures
│ ├── 02.03-sine.png
│ ├── 02.04-digit0.png
│ └── 02.04-digits0-9.png
├── README.md
├── requirements.txt
└── tools
├── add_book_info.py
├── add_navigation.py
├── fix_kernelspec.py
├── generate_contents.py
└── README.md
7 directories, 82 files
MATLAB与机器学习实例源代码完整版，一键运行，快速入门
文件清单
└── 使用opencv和python进行智能图像处理源代码
├── environment.yml
├── LICENSE
├── notebooks
│ ├── 00.00-Preface.ipynb
│ ├── 00.01-Foreword-by-Ariel-Rokem.ipynb
│ ├── 01.00-A-Taste-of-Machine-Learning.ipynb
│ ├── 02.00-Working-with-Data-in-OpenCV.ipynb
│ ├── 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb
│ ├── 02.02-Loading-External-Datasets-in-Python.ipynb
│ ├── 02.03-Visualizing-Data-Using-Matplotlib.ipynb
│ ├── 02.04-Visualizing-Data-from-an-External-Dataset.ipynb
│ ├── 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C .ipynb
│ ├── 03.00-First-Steps-in-Supervised-Learning.ipynb
│ ├── 03.01-Measuring-Model-Performance-with-Scoring-Functions.ipynb
│ ├── 03.02-Understanding-the-k-NN-Algorithm.ipynb
│ ├── 03.03-Using-Regression-Models-to-Predict-Continuous-Outcomes.ipynb
│ ├── 03.04-Applying-Lasso-and-Ridge-Regression.ipynb
│ ├── 03.05-Classifying-Iris-Species-Using-Logistic-Regression.ipynb
│ ├── 04.00-Representing-Data-and-Engineering-Features.ipynb
│ ├── 04.01-Preprocessing-Data.ipynb
│ ├── 04.02-Reducing-the-Dimensionality-of-the-Data.ipynb
│ ├── 04.03-Representing-Categorical-Variables.ipynb
│ ├── 04.04-Represening-Text-Features.ipynb
│ ├── 04.05-Representing-Images.ipynb
│ ├── 05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb
│ ├── 05.01-Building-Our-First-Decision-Tree.ipynb
│ ├── 05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb
│ ├── 05.03-Using-Decision-Trees-for-Regression.ipynb
│ ├── 06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb
│ ├── 06.01-Implementing-Your-First-Support-Vector-Machine.ipynb
│ ├── 06.02-Detecting-Pedestrians-in-the-Wild.ipynb
│ ├── 06.03-Additional-SVM-Exercises.ipynb
│ ├── 07.00-Implementing-a-Spam-Filter-with-Bayesian-Learning.ipynb
│ ├── 07.01-Implementing-Our-First-Bayesian-Classifier.ipynb
│ ├── 07.02-Classifying-Emails-Using-Naive-Bayes.ipynb
│ ├── 08.00-Discovering-Hidden-Structures-with-Unsupervised-Learning.ipynb
│ ├── 08.01-Understanding-k-Means-Clustering.ipynb
│ ├── 08.02-Compressing-Color-Images-Using-k-Means.ipynb
│ ├── 08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb
│ ├── 08.04-Implementing-Agglomerative-Hierarchical-Clustering.ipynb
│ ├── 09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb
│ ├── 09.01-Understanding-perceptrons.ipynb
│ ├── 09.02-Implementing-a-Multi-Layer-Perceptron-in-OpenCV.ipynb
│ ├── 09.03-Getting-Acquainted-with-Deep-Learning.ipynb
│ ├── 09.04-Training-an-MLP-in-OpenCV-to-Classify-Handwritten-Digits.ipynb
│ ├── 09.05-Training-a-Deep-Neural-Net-to-Classify-Handwritten-Digits-Using-Keras.ipynb
│ ├── 10.00-Combining-Different-Algorithms-Into-an-Ensemble.ipynb
│ ├── 10.01-Understanding-Ensemble-Methods.ipynb
│ ├── 10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb
│ ├── 10.03-Using-Random-Forests-for-Face-Recognition.ipynb
│ ├── 10.04-Implementing-AdaBoost.ipynb
│ ├── 10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb
│ ├── 11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb
│ ├── 11.01-Evaluating-a-Model.ipynb
│ ├── 11.02-Understanding-Cross-Validation-Bootstrapping-and-McNemar's-Test.ipynb
│ ├── 11.03-Tuning-Hyperparameters-with-Grid-Search.ipynb
│ ├── 11.04-Chaining-Algorithms-Together-to-Form-a-Pipeline.ipynb
│ ├── 12.00-Wrapping-Up.ipynb
│ ├── data
│ │ ├── chapter6
│ │ │ ├── pedestrians128x64.tar.gz
│ │ │ ├── pedestrians_neg.tar.gz
│ │ │ └── pedestrian_test.jpg
│ │ ├── chapter7
│ │ │ ├── beck-s.tar.gz
│ │ │ ├── BG.tar.gz
│ │ │ ├── farmer-d.tar.gz
│ │ │ ├── GP.tar.gz
│ │ │ ├── kaminski-v.tar.gz
│ │ │ ├── kitchen-l.tar.gz
│ │ │ ├── lokay-m.tar.gz
│ │ │ ├── SH.tar.gz
│ │ │ └── williams-w3.tar.gz
│ │ ├── cover.jpg
│ │ ├── haarcascade_frontalface_default.xml
│ │ └── lena.jpg
│ └── figures
│ ├── 02.03-sine.png
│ ├── 02.04-digit0.png
│ └── 02.04-digits0-9.png
├── README.md
├── requirements.txt
└── tools
├── add_book_info.py
├── add_navigation.py
├── fix_kernelspec.py
├── generate_contents.py
└── README.md
7 directories, 82 files