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文件列表
1 - 1 - Welcome (7 min).mkv
12.3 MB
1 - 2 - What is Machine Learning_ (7 min).mkv
9.7 MB
1 - 3 - Supervised Learning (12 min).mkv
13.9 MB
1 - 4 - Unsupervised Learning (14 min).mkv
17.2 MB
10 - 1 - Deciding What to Try Next (6 min).mkv
7.1 MB
10 - 2 - Evaluating a Hypothesis (8 min).mkv
8.8 MB
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
15.6 MB
10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv
9.3 MB
10 - 5 - Regularization and Bias_Variance (11 min).mkv
13.0 MB
10 - 6 - Learning Curves (12 min).mkv
13.4 MB
10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
8.5 MB
11 - 1 - Prioritizing What to Work On (10 min).mkv
11.6 MB
11 - 2 - Error Analysis (13 min).mkv
16.0 MB
11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
13.7 MB
11 - 4 - Trading Off Precision and Recall (14 min).mkv
16.5 MB
11 - 5 - Data For Machine Learning (11 min).mkv
13.3 MB
12 - 1 - Optimization Objective (15 min).mkv
17.2 MB
12 - 2 - Large Margin Intuition (11 min).mkv
12.2 MB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
22.6 MB
12 - 4 - Kernels I (16 min).mkv
18.2 MB
12 - 5 - Kernels II (16 min).mkv
18.0 MB
12 - 6 - Using An SVM (21 min).mkv
24.8 MB
13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv
3.9 MB
13 - 2 - K-Means Algorithm (13 min).mkv
14.3 MB
13 - 3 - Optimization Objective (7 min)(1).mkv
8.4 MB
13 - 3 - Optimization Objective (7 min).mkv
8.4 MB
13 - 4 - Random Initialization (8 min).mkv
9.0 MB
13 - 5 - Choosing the Number of Clusters (8 min).mkv
9.7 MB
14 - 1 - Motivation I_ Data Compression (10 min).mkv
14.8 MB
14 - 2 - Motivation II_ Visualization (6 min).mkv
6.5 MB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
10.8 MB
14 - 4 - Principal Component Analysis Algorithm (15 min).mkv
18.4 MB
14 - 5 - Choosing the Number of Principal Components (11 min).mkv
12.2 MB
14 - 6 - Reconstruction from Compressed Representation (4 min).mkv
5.2 MB
14 - 7 - Advice for Applying PCA (13 min).mkv
15.2 MB
15 - 1 - Problem Motivation (8 min).mkv
8.6 MB
15 - 2 - Gaussian Distribution (10 min).mkv
12.1 MB
15 - 3 - Algorithm (12 min).mkv
14.4 MB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
15.7 MB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
9.6 MB
15 - 6 - Choosing What Features to Use (12 min).mkv
14.6 MB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv
16.5 MB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv
16.9 MB
16 - 1 - Problem Formulation (8 min).mkv
11.1 MB
16 - 2 - Content Based Recommendations (15 min).mkv
17.5 MB
16 - 3 - Collaborative Filtering (10 min).mkv
12.2 MB
16 - 4 - Collaborative Filtering Algorithm (9 min).mkv
10.7 MB
16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
10.0 MB
16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv
10.0 MB
17 - 1 - Learning With Large Datasets (6 min).mkv
6.7 MB
17 - 2 - Stochastic Gradient Descent (13 min).mkv
15.9 MB
17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
7.6 MB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv
13.8 MB
17 - 5 - Online Learning (13 min).mkv
15.4 MB
17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
16.6 MB
18 - 1 - Problem Description and Pipeline (7 min).mkv
8.2 MB
18 - 2 - Sliding Windows (15 min).mkv
17.1 MB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv
19.5 MB
18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
16.7 MB
19 - 1 - Summary and Thank You (5 min).mkv
6.3 MB
2 - 1 - Model Representation (8 min).mkv
9.3 MB
2 - 2 - Cost Function (8 min).mkv
9.3 MB
2 - 3 - Cost Function - Intuition I (11 min).mkv
12.6 MB
2 - 4 - Cost Function - Intuition II (9 min).mkv
11.8 MB
2 - 5 - Gradient Descent (11 min).mkv
14.0 MB
2 - 6 - Gradient Descent Intuition (12 min).mkv
13.5 MB
2 - 7 - GradientDescentForLinearRegression (6 min).mkv
12.6 MB
2 - 8 - What_'s Next (6 min).mkv
6.3 MB
3 - 1 - Matrices and Vectors (9 min).mkv
9.9 MB
3 - 2 - Addition and Scalar Multiplication (7 min).mkv
7.7 MB
3 - 3 - Matrix Vector Multiplication (14 min).mkv
15.5 MB
3 - 4 - Matrix Matrix Multiplication (11 min).mkv
13.0 MB
3 - 5 - Matrix Multiplication Properties (9 min).mkv
10.1 MB
3 - 6 - Inverse and Transpose (11 min).mkv
13.3 MB
4 - 1 - Multiple Features (8 min).mkv
9.1 MB
4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
6.0 MB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
9.8 MB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
9.6 MB
4 - 5 - Features and Polynomial Regression (8 min).mkv
8.5 MB
4 - 6 - Normal Equation (16 min).mkv
17.7 MB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv
6.5 MB
5 - 1 - Basic Operations (14 min).mkv
18.4 MB
5 - 2 - Moving Data Around (16 min).mkv
21.5 MB
5 - 3 - Computing on Data (13 min).mkv
15.8 MB
5 - 4 - Plotting Data (10 min).mkv
13.8 MB
5 - 5 - Control Statements_ for, while, if statements (13 min).mkv
17.1 MB
5 - 6 - Vectorization (14 min).mkv
16.6 MB
5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv
5.7 MB
6 - 1 - Classification (8 min).mkv
9.1 MB
6 - 2 - Hypothesis Representation (7 min).mkv
8.6 MB
6 - 3 - Decision Boundary (15 min).mkv
17.3 MB
6 - 4 - Cost Function (11 min).mkv
13.5 MB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv
12.4 MB
6 - 6 - Advanced Optimization (14 min).mkv
18.8 MB
6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv
7.2 MB
7 - 1 - The Problem of Overfitting (10 min).mkv
11.5 MB
7 - 2 - Cost Function (10 min).mkv
12.0 MB
7 - 3 - Regularized Linear Regression (11 min).mkv
12.4 MB
7 - 4 - Regularized Logistic Regression (9 min).mkv
11.3 MB
8 - 1 - Non-linear Hypotheses (10 min).mkv
11.3 MB
8 - 2 - Neurons and the Brain (8 min).mkv
10.2 MB
8 - 3 - Model Representation I (12 min).mkv
14.0 MB
8 - 4 - Model Representation II (12 min).mkv
13.9 MB
8 - 5 - Examples and Intuitions I (7 min).mkv
8.2 MB
8 - 6 - Examples and Intuitions II (10 min).mkv
14.5 MB
8 - 7 - Multiclass Classification (4 min).mkv
5.0 MB
9 - 1 - Cost Function (7 min).mkv
7.9 MB
9 - 2 - Backpropagation Algorithm (12 min).mkv
14.4 MB
9 - 3 - Backpropagation Intuition (13 min).mkv
16.0 MB
9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv
9.7 MB
9 - 5 - Gradient Checking (12 min).mkv
14.0 MB
9 - 6 - Random Initialization (7 min).mkv
7.8 MB
9 - 7 - Putting It Together (14 min).mkv
16.9 MB
9 - 8 - Autonomous Driving (7 min).mkv
15.5 MB
pdf/Lecture1.pdf
3.5 MB
pdf/Lecture10.pdf
1.6 MB
pdf/Lecture11.pdf
509.6 kB
pdf/Lecture12.pdf
2.4 MB
pdf/Lecture13.pdf
2.3 MB
pdf/Lecture14.pdf
1.7 MB
pdf/Lecture15.pdf
3.5 MB
pdf/Lecture16.pdf
1.5 MB
pdf/Lecture17.pdf
2.1 MB
pdf/Lecture18.pdf
2.1 MB
pdf/Lecture2.pdf
3.0 MB
pdf/Lecture3.pdf
1.9 MB
pdf/Lecture4.pdf
1.8 MB
pdf/Lecture5.pdf
248.2 kB
pdf/Lecture6.pdf
2.2 MB
pdf/Lecture7.pdf
2.5 MB
pdf/Lecture8.pdf
5.2 MB
pdf/Lecture9.pdf
3.5 MB
ppt/Lecture1.pptx
4.2 MB
ppt/Lecture10.pptx
3.5 MB
ppt/Lecture11.pptx
2.0 MB
ppt/Lecture12.pptx
5.6 MB
ppt/Lecture13.pptx
2.9 MB
ppt/Lecture14.pptx
3.8 MB
ppt/Lecture15.pptx
6.3 MB
ppt/Lecture16.pptx
3.8 MB
ppt/Lecture17.pptx
4.0 MB
ppt/Lecture18.pptx
6.4 MB
ppt/Lecture2.pptx
5.6 MB
ppt/Lecture3.pptx
5.2 MB
ppt/Lecture4.pptx
4.6 MB
ppt/Lecture5.pptx
417.1 kB
ppt/Lecture6.pptx
4.0 MB
ppt/Lecture7.pptx
2.7 MB
ppt/Lecture8.pptx
42.3 MB
ppt/Lecture9.pptx
5.2 MB
推荐播放器/PotPlayer_1.6.51270.zip
20.1 MB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/ex8.pdf
270.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/checkCostFunction.m
1.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/cofiCostFunc.m
2.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/computeNumericalGradient.m
1.1 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/estimateGaussian.m
986 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/ex8.m
3.8 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/ex8_cofi.m
7.1 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/ex8_movieParams.mat
201.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/ex8_movies.mat
223.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/ex8data1.mat
9.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/ex8data2.mat
93.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/fmincg.m
8.7 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/loadMovieList.m
651 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/movie_ids.txt
48.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/multivariateGaussian.m
829 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/normalizeRatings.m
460 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/octave-core
40.5 MB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/selectThreshold.m
1.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/submit.m
9.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/submitWeb.m
10.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Anomaly Detection and Recommender Systems/mlclass-ex8/visualizeFit.m
582 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/.dtree.py.swp
24.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/.testdtree.py.swp
20.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/breast-cancer-wisconsin.names
5.7 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/build.log
0 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/call_graph.png
240.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/data.csv
2.1 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/dtree.py
23.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/dtree.pyc
27.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/dtree1.py
22.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/dttasks.py
11.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/dttasks.pyc
14.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/events.sqlite
264.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/hw1.bat
56 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/hw1.pdf
284.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/Makefile
41 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/noisy.dat
2.1 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/submit.sh
563 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/testdtree.py
27.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/testdtree.pyc
29.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/view.txt
4.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/.hmm.py.swp
36.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/classify.py
3.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/classify.pyc
3.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/dataset.py
4.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/dataset.pyc
4.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/events.sqlite
39.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/hmm.py
19.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/hmm.pyc
18.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/hw4.bat
56 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/Makefile
41 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_no_momentum.data
480.8 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_small.data
159 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_with_momentum.data
480.8 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/submit.sh
598 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/task_hmm.py
6.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/task_hmm.pyc
7.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/test_hmm.py
5.0 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/test_hmm.pyc
6.0 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/util.py
1.9 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/util.pyc
2.7 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/viterbi.py
2.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/viterbi.pyc
2.7 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_all.data
406.8 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_bos_la.data
202.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_bos_sea.data
213.7 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/ex7.pdf
750.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/bird_small.mat
45.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/bird_small.png
33.0 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/computeCentroids.m
1.3 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/displayData.m
1.5 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/drawLine.m
232 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/ex7.m
5.6 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/ex7_pca.m
7.2 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/ex7data1.mat
995 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/ex7data2.mat
4.8 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/ex7faces.mat
11.0 MB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/featureNormalize.m
510 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/findClosestCentroids.m
1.1 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/kMeansInitCentroids.m
668 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/octave-core
42.6 MB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/pca.m
900 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/plotDataPoints.m
434 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/plotProgresskMeans.m
840 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/projectData.m
969 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/recoverData.m
1.0 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/runkMeans.m
2.0 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/submit.m
8.8 kB
教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/submitWeb.m
10.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Lectures/aimlcs229/AI-classes.pdf
36.0 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Lectures/aimlcs229/compute_kernel_matrix.txt
222 Bytes
教程和笔记/Stanford-Machine-Learning-Course-master/Lectures/aimlcs229/cs229-cvxopt.pdf
152.4 kB
教程和笔记/Stanford-Machine-Learning-Course-master/Lectures/aimlcs229/cs229-cvxopt2.pdf
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教程和笔记/Stanford-Machine-Learning-Course-master/Lectures/aimlcs229/cs229-gp.pdf
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机器学习课程2014源代码/mlclass-ex5-jin/ex5.m
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机器学习课程2014源代码/mlclass-ex6-jin/ex6.m
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机器学习课程2014源代码/整合pdf/Programming Exercise(机器学习2014练习).pdf
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机器学习课程2014源代码/整合pdf/源代码目录.docx
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