Machine Learning for OpenCV__
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2020-10-07
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Machine Learning for OpenCV.rar （84个子文件） 
Machine Learning for OpenCV 
Machine Learning for OpenCV.pdf 12.1MB opencv-machine-learning-master notebooks 
00.00-Preface.ipynb 9KB 05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb 67KB figures 
02.03-sine.png 1KB 02.04-digit0.png 4KB 02.04-digits0-9.png 8KB 10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb 10KB 02.03-Visualizing-Data-Using-Matplotlib.ipynb 27KB 10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb 250KB 11.02-Understanding-Cross-Validation-Bootstrapping-and-McNemar's-Test.ipynb 34KB 09.04-Training-an-MLP-in-OpenCV-to-Classify-Handwritten-Digits.ipynb 86KB 09.02-Implementing-a-Multi-Layer-Perceptron-in-OpenCV.ipynb 92KB 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20KB 07.00-Implementing-a-Spam-Filter-with-Bayesian-Learning.ipynb 4KB 06.03-Additional-SVM-Exercises.ipynb 406KB 03.03-Using-Regression-Models-to-Predict-Continuous-Outcomes.ipynb 117KB 09.05-Training-a-Deep-Neural-Net-to-Classify-Handwritten-Digits-Using-Keras.ipynb 13KB 09.01-Understanding-perceptrons.ipynb 210KB 11.01-Evaluating-a-Model.ipynb 10KB 09.03-Getting-Acquainted-with-Deep-Learning.ipynb 13KB 05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb 9KB 00.01-Foreword-by-Ariel-Rokem.ipynb 5KB 08.04-Implementing-Agglomerative-Hierarchical-Clustering.ipynb 16KB 08.01-Understanding-k-Means-Clustering.ipynb 389KB 06.02-Detecting-Pedestrians-in-the-Wild.ipynb 383KB 10.03-Using-Random-Forests-for-Face-Recognition.ipynb 175KB 10.04-Implementing-AdaBoost.ipynb 149KB 03.01-Measuring-Model-Performance-with-Scoring-Functions.ipynb 60KB 04.05-Representing-Images.ipynb 673KB 06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb 5KB 03.02-Understanding-the-k-NN-Algorithm.ipynb 66KB 09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb 5KB 12.00-Wrapping-Up.ipynb 14KB 03.00-First-Steps-in-Supervised-Learning.ipynb 6KB 11.03-Tuning-Hyperparameters-with-Grid-Search.ipynb 17KB 07.02-Classifying-Emails-Using-Naive-Bayes.ipynb 27KB 05.01-Building-Our-First-Decision-Tree.ipynb 102KB 01.00-A-Taste-of-Machine-Learning.ipynb 19KB 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 20KB 04.04-Represening-Text-Features.ipynb 7KB 11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb 4KB 11.04-Chaining-Algorithms-Together-to-Form-a-Pipeline.ipynb 13KB 02.02-Loading-External-Datasets-in-Python.ipynb 7KB 04.01-Preprocessing-Data.ipynb 15KB 04.03-Representing-Categorical-Variables.ipynb 7KB 08.00-Discovering-Hidden-Structures-with-Unsupervised-Learning.ipynb 5KB 05.03-Using-Decision-Trees-for-Regression.ipynb 40KB data haarcascade_frontalface_default.xml 908KB chapter6 
pedestrians_neg.tar.gz 2.35MB
pedestrian_test.jpg 54KB pedestrians128x64.tar.gz 10.34MB lena.jpg 8KB chapter7 williams-w3.tar.gz 841KB kaminski-v.tar.gz 2.58MB lokay-m.tar.gz 1.73MB GP.tar.gz 16.32MB BG.tar.gz 11.62MB beck-s.tar.gz 1.15MB SH.tar.gz 12.05MB farmer-d.tar.gz 1.49MB kitchen-l.tar.gz 5.06MB cover.jpg 14KB 10.01-Understanding-Ensemble-Methods.ipynb 21KB 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 6KB 03.05-Classifying-Iris-Species-Using-Logistic-Regression.ipynb 52KB 08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb 16KB 04.00-Representing-Data-and-Engineering-Features.ipynb 7KB 04.02-Reducing-the-Dimensionality-of-the-Data.ipynb 169KB 07.01-Implementing-Our-First-Bayesian-Classifier.ipynb 210KB 02.00-Working-with-Data-in-OpenCV.ipynb 8KB 10.00-Combining-Different-Algorithms-Into-an-Ensemble.ipynb 5KB 03.04-Applying-Lasso-and-Ridge-Regression.ipynb 116KB 06.01-Implementing-Your-First-Support-Vector-Machine.ipynb 136KB 08.02-Compressing-Color-Images-Using-k-Means.ipynb 768KB
requirements.txt 151B LICENSE 1KB tools add_book_info.py 2KB generate_contents.py 2KB add_navigation.py 2KB README.md 471B fix_kernelspec.py 526B environment.yml 265B README.md 17KB .gitignore 1KB资源评论
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