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  • OpenCV视频计算机视觉处理深度学习人工智能图像检测试C++

    OpenCV视频计算机视觉处理深度学习人工智能图像检测试C++ 最后编辑:2020-08-15
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    OpenCV视频计算机视觉处理深度学习人工智能图像检测试C++插图

    课程说明-玛丽圈

    OpenCV视频计算机视觉处理深度学习人工智能图像检测试C++9650

    OpenCV视频计算机视觉处理深度学习人工智能图像检测试C++插图2

     

    课程内容-玛丽圈资源网

    ——/A08 OpenCV视频计算机视觉处理深度学习人工智能图像检测试C++/
    ├──09-计算机视觉课程
    | ├──第八课
    | | ├──第8课cv_julyonline_caption.pdf 59.01M
    | | └──第八课.mkv 749.14M
    | ├──第二课
    | | ├──cv第二次资料
    | | ├──cv_第一二讲 – 副本.pdf 2.18M
    | | ├──cv第二次资料.rar 11.06M
    | | └──第二课.mkv 746.57M
    | ├──第九课
    | | ├──cv_lec9_3D_v0.pdf 9.95M
    | | ├──cv_lec9_3D_v1.pdf 11.92M
    | | ├──第九课上.mkv 645.85M
    | | └──第九课下.mkv 117.84M
    | ├──第六课
    | | ├──lesson6_cnn_and_transfer_learning (1).zip 23.28M
    | | ├──第六课上.mkv 646.35M
    | | ├──第六课下.mkv 155.60M
    | | └──卷积神经网络与迁移学习.pdf 4.10M
    | ├──第七课
    | | ├──第七课.mkv 560.99M
    | | └──计算机视觉班第7课–图像检索.pdf 26.52M
    | ├──第三课
    | | ├──cv_第三讲.pdf 5.10M
    | | ├──第三课上.mkv 594.48M
    | | └──第三课下.mkv 223.66M
    | ├──第十课
    | | ├──第十课.mkv 633.77M
    | | └──计算机视觉班lec10.pdf 1.31M
    | ├──第四课
    | | ├──cv_第四讲.pdf 3.45M
    | | ├──第四课上.mkv 848.83M
    | | └──第四课下.mkv 15.46M
    | ├──第五讲
    | | ├──cv_第五讲 – Copy.pdf 3.50M
    | | ├──cv第五次资料.rar 115.76M
    | | └──第五课.mkv 605.90M
    | ├──第一课
    | | ├──cv_第一二讲 – 副本.pdf 2.18M
    | | ├──cv第一次资料.rar 141.76M
    | | ├──第一课.mkv 831.76M
    | | └──截至2012年-图像处理与计算机视觉基础总结.docx 65.22kb
    | └──截至2012年-图像处理与计算机视觉基础总结.docx 65.22kb
    ├──DL_5月深度学习班
    | ├──第10课 更多框架
    | | ├──5月班第10课_framework.pdf 22.02M
    | | └──第10课 更多框架.avi 429.37M
    | ├──第1课 机器学习中数学基础
    | | ├──第1课 机器学习中数学基础.avi 609.35M
    | | └──五月班第一次课件:机器学习中数学基础 (1).pdf 1.29M
    | ├──第2课 高效计算基础与图像线性分类器
    | | ├──5月班第2课课件:高效计算基础与图像线性分类器.pdf 32.88M
    | | ├──image linear classification.zip 163.57M
    | | ├──numpy_operations.ipynb 207.42kb
    | | └──第2课 高效计算基础与图像线性分类器.avi 677.91M
    | ├──第3课 梯度下降法与反向传播
    | | ├──5月班第3课课件:梯度下降法与反向传播 (1).pdf 1.13M
    | | └──第3课 梯度下降法与反向传播.avi 438.73M
    | ├──第4课 CNN与常用框架
    | | ├──5月深度学习班第4课–CNN,典型网络结构与常用框架.pdf 6.97M
    | | └──第4课 CNN与常用框架.avi 650.80M
    | ├──第5课 CNN训练注意事项与框架使用
    | | ├──5月班第5次课 – caffe TensorFlow使用与CNN训练注意事项.pdf 17.48M
    | | └──第5课 CNN训练注意事项与框架使用.avi 743.32M
    | ├──第6课 CNN推展案例
    | | ├──5月班第6次课 – CNN扩展 图像识别与定位 物体检测 NeuralStyle.pdf 48.34M
    | | └──第6课 CNN推展案例.avi 662.57M
    | ├──第7课 RNN介绍
    | | ├──5月班第7课课件_rnn_intrduction.pdf 8.48M
    | | └──第7课 RNN介绍.avi 362.89M
    | ├──第8课 RNN应用
    | | ├──5月班第8课_rnn_appliacation.pdf 22.52M
    | | └──第8课 RNN应用.avi 531.08M
    | └──第9课 更多的网络类型
    | | ├──5月班第9次课课件_more_about_nn.pdf 4.18M
    | | └──第9课 更多的网络类型.avi 483.73M
    ├──ML_机器学习其他资料
    | ├──2014斯坦福大学机器学习mkv视频
    | | ├──pdf
    | | ├──ppt
    | | ├──机器学习课程2014源代码
    | | ├──教程和笔记
    | | ├──推荐播放器
    | | ├──网易视频教程
    | | ├──1 – 1 – Welcome (7 min).mkv 11.69M
    | | ├──1 – 2 – What is Machine Learning_ (7 min).mkv 9.25M
    | | ├──1 – 3 – Supervised Learning (12 min).mkv 13.25M
    | | ├──1 – 4 – Unsupervised Learning (14 min).mkv 16.45M
    | | ├──10 – 1 – Deciding What to Try Next (6 min).mkv 6.78M
    | | ├──10 – 2 – Evaluating a Hypothesis (8 min).mkv 8.36M
    | | ├──10 – 3 – Model Selection and Train_Validation_Test Sets (12 min).mkv 14.92M
    | | ├──10 – 4 – Diagnosing Bias vs. Variance (8 min).mkv 8.86M
    | | ├──10 – 5 – Regularization and Bias_Variance (11 min).mkv 12.42M
    | | ├──10 – 6 – Learning Curves (12 min).mkv 12.74M
    | | ├──10 – 7 – Deciding What to Do Next Revisited (7 min).mkv 8.08M
    | | ├──11 – 1 – Prioritizing What to Work On (10 min).mkv 11.03M
    | | ├──11 – 2 – Error Analysis (13 min).mkv 15.22M
    | | ├──11 – 3 – Error Metrics for Skewed Classes (12 min).mkv 13.07M
    | | ├──11 – 4 – Trading Off Precision and Recall (14 min).mkv 15.77M
    | | ├──11 – 5 – Data For Machine Learning (11 min).mkv 12.70M
    | | ├──12 – 1 – Optimization Objective (15 min).mkv 16.42M
    | | ├──12 – 2 – Large Margin Intuition (11 min).mkv 11.65M
    | | ├──12 – 3 – Mathematics Behind Large Margin Classification (Optional) (20 min).mkv 21.51M
    | | ├──12 – 4 – Kernels I (16 min).mkv 17.32M
    | | ├──12 – 5 – Kernels II (16 min).mkv 17.20M
    | | ├──12 – 6 – Using An SVM (21 min).mkv 23.63M
    | | ├──13 – 1 – Unsupervised Learning_ Introduction (3 min).mkv 3.76M
    | | ├──13 – 2 – K-Means Algorithm (13 min).mkv 13.61M
    | | ├──13 – 3 – Optimization Objective (7 min)(1).mkv 8.04M
    | | ├──13 – 3 – Optimization Objective (7 min).mkv 8.03M
    | | ├──13 – 4 – Random Initialization (8 min).mkv 8.56M
    | | ├──13 – 5 – Choosing the Number of Clusters (8 min).mkv 9.28M
    | | ├──14 – 1 – Motivation I_ Data Compression (10 min).mkv 14.15M
    | | ├──14 – 2 – Motivation II_ Visualization (6 min).mkv 6.22M
    | | ├──14 – 3 – Principal Component Analysis Problem Formulation (9 min).mkv 10.32M
    | | ├──14 – 4 – Principal Component Analysis Algorithm (15 min).mkv 17.55M
    | | ├──14 – 5 – Choosing the Number of Principal Components (11 min).mkv 11.67M
    | | ├──14 – 6 – Reconstruction from Compressed Representation (4 min).mkv 4.92M
    | | ├──14 – 7 – Advice for Applying PCA (13 min).mkv 14.50M
    | | ├──15 – 1 – Problem Motivation (8 min).mkv 8.23M
    | | ├──15 – 2 – Gaussian Distribution (10 min).mkv 11.53M
    | | ├──15 – 3 – Algorithm (12 min).mkv 13.77M
    | | ├──15 – 4 – Developing and Evaluating an Anomaly Detection System (13 min).mkv 14.96M
    | | ├──15 – 5 – Anomaly Detection vs. Supervised Learning (8 min).mkv 9.17M
    | | ├──15 – 6 – Choosing What Features to Use (12 min).mkv 13.93M
    | | ├──15 – 7 – Multivariate Gaussian Distribution (Optional) (14 min).mkv 15.72M
    | | ├──15 – 8 – Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv 16.12M
    | | ├──16 – 1 – Problem Formulation (8 min).mkv 10.57M
    | | ├──16 – 2 – Content Based Recommendations (15 min).mkv 16.71M
    | | ├──16 – 3 – Collaborative Filtering (10 min).mkv 11.60M
    | | ├──16 – 4 – Collaborative Filtering Algorithm (9 min).mkv 10.18M
    | | ├──16 – 5 – Vectorization_ Low Rank Matrix Factorization (8 min).mkv 9.55M
    | | ├──16 – 6 – Implementational Detail_ Mean Normalization (9 min).mkv 9.58M
    | | ├──17 – 1 – Learning With Large Datasets (6 min).mkv 6.41M
    | | ├──17 – 2 – Stochastic Gradient Descent (13 min).mkv 15.12M
    | | ├──17 – 3 – Mini-Batch Gradient Descent (6 min).mkv 7.22M
    | | ├──17 – 4 – Stochastic Gradient Descent Convergence (12 min).mkv 13.15M
    | | ├──17 – 5 – Online Learning (13 min).mkv 14.72M
    | | ├──17 – 6 – Map Reduce and Data Parallelism (14 min).mkv 15.84M
    | | ├──18 – 1 – Problem Description and Pipeline (7 min).mkv 7.81M
    | | ├──18 – 2 – Sliding Windows (15 min).mkv 16.30M
    | | ├──18 – 3 – Getting Lots of Data and Artificial Data (16 min).mkv 18.57M
    | | ├──18 – 4 – Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv 15.90M
    | | ├──19 – 1 – Summary and Thank You (5 min).mkv 6.02M
    | | ├──2 – 1 – Model Representation (8 min).mkv 8.86M
    | | ├──2 – 2 – Cost Function (8 min).mkv 8.91M
    | | ├──2 – 3 – Cost Function – Intuition I (11 min).mkv 12.06M
    | | ├──2 – 4 – Cost Function – Intuition II (9 min).mkv 11.22M
    | | ├──2 – 5 – Gradient Descent (11 min).mkv 13.32M
    | | ├──2 – 6 – Gradient Descent Intuition (12 min).mkv 12.84M
    | | ├──2 – 7 – GradientDescentForLinearRegression (6 min).mkv 12.02M
    | | ├──2 – 8 – What_’s Next (6 min).mkv 5.99M
    | | ├──3 – 1 – Matrices and Vectors (9 min).mkv 9.42M
    | | ├──3 – 2 – Addition and Scalar Multiplication (7 min).mkv 7.35M
    | | ├──3 – 3 – Matrix Vector Multiplication (14 min).mkv 14.78M
    | | ├──3 – 4 – Matrix Matrix Multiplication (11 min).mkv 12.42M
    | | ├──3 – 5 – Matrix Multiplication Properties (9 min).mkv 9.67M
    | | ├──3 – 6 – Inverse and Transpose (11 min).mkv 12.69M
    | | ├──4 – 1 – Multiple Features (8 min).mkv 8.71M
    | | ├──4 – 2 – Gradient Descent for Multiple Variables (5 min).mkv 5.71M
    | | ├──4 – 3 – Gradient Descent in Practice I – Feature Scaling (9 min).mkv 9.32M
    | | ├──4 – 4 – Gradient Descent in Practice II – Learning Rate (9 min).mkv 9.13M
    | | ├──4 – 5 – Features and Polynomial Regression (8 min).mkv 8.15M
    | | ├──4 – 6 – Normal Equation (16 min).mkv 16.88M
    | | ├──4 – 7 – Normal Equation Noninvertibility (Optional) (6 min).mkv 6.15M
    | | ├──5 – 1 – Basic Operations (14 min).mkv 17.50M
    | | ├──5 – 2 – Moving Data Around (16 min).mkv 20.52M
    | | ├──5 – 3 – Computing on Data (13 min).mkv 15.04M
    | | ├──5 – 4 – Plotting Data (10 min).mkv 13.17M
    | | ├──5 – 5 – Control Statements_ for, while, if statements (13 min).mkv 16.29M
    | | ├──5 – 6 – Vectorization (14 min).mkv 15.88M
    | | ├──5 – 7 – Working on and Submitting Programming Exercises (4 min).mkv 5.41M
    | | ├──6 – 1 – Classification (8 min).mkv 8.65M
    | | ├──6 – 2 – Hypothesis Representation (7 min).mkv 8.23M
    | | ├──6 – 3 – Decision Boundary (15 min).mkv 16.51M
    | | ├──6 – 4 – Cost Function (11 min).mkv 12.92M
    | | ├──6 – 5 – Simplified Cost Function and Gradient Descent (10 min).mkv 11.80M
    | | ├──6 – 6 – Advanced Optimization (14 min).mkv 17.95M
    | | ├──6 – 7 – Multiclass Classification_ One-vs-all (6 min).mkv 6.83M
    | | ├──7 – 1 – The Problem of Overfitting (10 min).mkv 11.00M
    | | ├──7 – 2 – Cost Function (10 min).mkv 11.48M
    | | ├──7 – 3 – Regularized Linear Regression (11 min).mkv 11.84M
    | | ├──7 – 4 – Regularized Logistic Regression (9 min).mkv 10.77M
    | | ├──8 – 1 – Non-linear Hypotheses (10 min).mkv 10.73M
    | | ├──8 – 2 – Neurons and the Brain (8 min).mkv 9.77M
    | | ├──8 – 3 – Model Representation I (12 min).mkv 13.32M
    | | ├──8 – 4 – Model Representation II (12 min).mkv 13.27M
    | | ├──8 – 5 – Examples and Intuitions I (7 min).mkv 7.78M
    | | ├──8 – 6 – Examples and Intuitions II (10 min).mkv 13.84M
    | | ├──8 – 7 – Multiclass Classification (4 min).mkv 4.77M
    | | ├──9 – 1 – Cost Function (7 min).mkv 7.56M
    | | ├──9 – 2 – Backpropagation Algorithm (12 min).mkv 13.75M
    | | ├──9 – 3 – Backpropagation Intuition (13 min).mkv 15.25M
    | | ├──9 – 4 – Implementation Note_ Unrolling Parameters (8 min).mkv 9.27M
    | | ├──9 – 5 – Gradient Checking (12 min).mkv 13.32M
    | | ├──9 – 6 – Random Initialization (7 min).mkv 7.46M
    | | ├──9 – 7 – Putting It Together (14 min).mkv 16.10M
    | | └──9 – 8 – Autonomous Driving (7 min).mkv 14.79M
    | ├──机器学习导论_42_上海交大(张志华)
    | | ├──1 基本概念.mp4 833.42M
    | | ├──10 核定义.mp4 840.46M
    | | ├──11 正定核性质.mp4 732.40M
    | | ├──12 正定核应用.mp4 766.98M
    | | ├──13 核主元分析.mp4 836.17M
    | | ├──14 主元分析.mp4 854.21M
    | | ├──15 主坐标分析.mp4 732.18M
    | | ├──16 期望最大算法.mp4 717.03M
    | | ├──17 概率PCA.mp4 659.33M
    | | ├──18 最大似然估计方法.mp4 747.24M
    | | ├──19 EM算法收敛性.mp4 911.58M
    | | ├──2 随机向量.mp4 783.70M
    | | ├──20 MDS方法.mp4 993.05M
    | | ├──21 MDS中加点方法.mp4 650.00M
    | | ├──22 矩阵次导数.mp4 684.66M
    | | ├──23 矩阵范数.mp4 822.33M
    | | ├──24 次导数.mp4 783.83M
    | | ├──25 spectral clustering.mp4 620.10M
    | | ├──26 K-means algorithm.mp4 802.07M
    | | ├──27 Matr-x Completion.mp4 737.15M
    | | ├──28 Fisher判别分析.mp4 918.01M
    | | ├──29 谱聚类1 .mp4 955.05M
    | | ├──3 随机向量性质.mp4 716.79M
    | | ├──30 谱聚类2.mp4 997.68M
    | | ├──31 Computational Methods1.mp4 904.69M
    | | ├──32 Computational Methods2.mp4 980.74M
    | | ├──33 Fisher Discriminant Analysis.mp4 976.99M
    | | ├──34 Kernel FDA.mp4 968.28M
    | | ├──35 Linear classification1.mp4 962.55M
    | | ├──36 Linear classification2.mp4 987.11M
    | | ├──37 Naive Bayes方法.mp4 988.37M
    | | ├──38 Support Vector Machines1.mp4 962.00M
    | | ├──39 Support Vector Machines2.mp4 931.91M
    | | ├──4 多元高斯分布.mp4 768.81M
    | | ├──40 SVM.mp4 932.42M
    | | ├──41 Boosting1.mp4 978.84M
    | | ├──42 Boosting2.mp4 981.56M
    | | ├──5 分布性质.mp4 561.94M
    | | ├──6 条件期望.mp4 789.45M
    | | ├──7 多项式分布.mp4 800.88M
    | | ├──8 多元高斯分布及应用.mp4 745.73M
    | | └──9 渐近性质.mp4 727.84M
    | ├──机器学习基石_国立台湾大学(林轩田)
    | | ├──1 – 1 – Course Introduction (10-58)(1).mp4 13.79M
    | | ├──1 – 2 – What is Machine Learning (18-28).mp4 15.94M
    | | ├──1 – 3 – Applications of Machine Learning (18-56)(1).mp4 22.31M
    | | ├──1 – 4 – Components of Machine Learning (11-45)(1).mp4 10.66M
    | | ├──1 – 5 – Machine Learning and Other Fields (10-21)(1).mp4 11.97M
    | | ├──10 – 1 – Logistic Regression Problem (14-33).mp4 11.94M
    | | ├──10 – 2 – Logistic Regression Error (15-58).mp4 11.96M
    | | ├──10 – 3 – Gradient of Logistic Regression Error (15-38).mp4 12.37M
    | | ├──10 – 4 – Gradient Descent (19-18)(1).mp4 14.91M
    | | ├──11 – 1 – Linear Models for Binary Classification (21-35).mp4 16.91M
    | | ├──11 – 2 – Stochastic Gradient Descent (11-39).mp4 9.96M
    | | ├──11 – 3 – Multiclass via Logistic Regression (14-18).mp4 11.28M
    | | ├──11 – 4 – Multiclass via Binary Classification (11-35).mp4 9.36M
    | | ├──12 – 1 – Quadratic Hypothesis (23-47).mp4 17.92M
    | | ├──12 – 2 – Nonlinear Transform (09-52).mp4 8.03M
    | | ├──12 – 3 – Price of Nonlinear Transform (15-37).mp4 12.55M
    | | ├──12 – 4 – Structured Hypothesis Sets (09-36).mp4 7.31M
    | | ├──13 – 1 – What is Overfitting- (10-45).mp4 9.01M
    | | ├──13 – 2 – The Role of Noise and Data Size (13-36).mp4 11.40M
    | | ├──13 – 3 – Deterministic Noise (14-07).mp4 11.92M
    | | ├──13 – 4 – Dealing with Overfitting (10-49).mp4 8.81M
    | | ├──14 – 1 – Regularized Hypothesis Set (19-16).mp4 15.18M
    | | ├──14 – 2 – Weight Decay Regularization (24-08).mp4 18.54M
    | | ├──14 – 3 – Regularization and VC Theory (08-15).mp4 7.14M
    | | ├──14 – 4 – General Regularizers (13-28).mp4 11.24M
    | | ├──15 – 1 – Model Selection Problem (16-00).mp4 13.26M
    | | ├──15 – 2 – Validation (13-24).mp4 10.47M
    | | ├──15 – 3 – Leave-One-Out Cross Validation (16-06).mp4 12.27M
    | | ├──15 – 4 – V-Fold Cross Validation (10-41).mp4 9.17M
    | | ├──16 – 1 – Occam-‘s Razor (10-08).mp4 8.21M
    | | ├──16 – 2 – Sampling Bias (11-50).mp4 10.26M
    | | ├──16 – 3 – Data Snooping (12-28).mp4 10.80M
    | | ├──16 – 4 – Power of Three (08-49).mp4 7.55M
    | | ├──2 – 1 – Perceptron Hypothesis Set (15-42).mp4 18.55M
    | | ├──2 – 2 – Perceptron Learning Algorithm (PLA) (19-46).mp4 16.61M
    | | ├──2 – 3 – Guarantee of PLA (12-37).mp4 14.45M
    | | ├──2 – 4 – Non-Separable Data (12-55).mp4 33.75M
    | | ├──3 – 1 – Learning with Different Output Space (17-26).mp4 16.16M
    | | ├──3 – 2 – Learning with Different Data Label (18-12).mp4 50.14M
    | | ├──3 – 3 – Learning with Different Protocol (11-09).mp4 31.41M
    | | ├──3 – 4 – Learning with Different Input Space (14-13).mp4 40.89M
    | | ├──4 – 1 – Learning is Impossible- (13-32).mp4 11.47M
    | | ├──4 – 2 – Probability to the Rescue (11-33).mp4 9.86M
    | | ├──4 – 3 – Connection to Learning (16-46).mp4 14.29M
    | | ├──4 – 4 – Connection to Real Learning (18-06).mp4 15.05M
    | | ├──5 – 1 – Recap and Preview (13-44).mp4 11.35M
    | | ├──5 – 2 – Effective Number of Lines (15-26).mp4 12.57M
    | | ├──5 – 3 – Effective Number of Hypotheses (16-17).mp4 13.12M
    | | ├──5 – 4 – Break Point (07-44).mp4 6.60M
    | | ├──6 – 1 – Restriction of Break Point (14-18).mp4 11.52M
    | | ├──6 – 2 – Bounding Function- Basic Cases (06-56).mp4 5.50M
    | | ├──6 – 3 – Bounding Function- Inductive Cases (14-47).mp4 11.64M
    | | ├──6 – 4 – A Pictorial Proof (16-01).mp4 12.85M
    | | ├──7 – 1 – Definition of VC Dimension (13-10).mp4 10.67M
    | | ├──7 – 2 – VC Dimension of Perceptrons (13-27).mp4 9.97M
    | | ├──7 – 3 – Physical Intuition of VC Dimension (6-11).mp4 5.16M
    | | ├──7 – 4 – Interpreting VC Dimension (17-13).mp4 13.55M
    | | ├──8 – 1 – Noise and Probabilistic Target (17-01).mp4 13.93M
    | | ├──8 – 2 – Error Measure (15-10).mp4 11.40M
    | | ├──8 – 3 – Algorithmic Error Measure (13-46).mp4 10.98M
    | | ├──8 – 4 – Weighted Classification (16-54).mp4 13.11M
    | | ├──9 – 1 – Linear Regression Problem (10-08).mp4 8.04M
    | | ├──9 – 2 – Linear Regression Algorithm (20-03).mp4 14.51M
    | | ├──9 – 3 – Generalization Issue (20-34).mp4 15.28M
    | | └──9 – 4 – Linear Regression for Binary Classification (11-23).mp4 9.05M
    | ├──机器学习技法_国立台湾大学(林轩田)
    | | ├──01_Linear_Support_Vector_Machine
    | | ├──02_Dual_Support_Vector_Machine
    | | ├──03_Kernel_Support_Vector_Machine
    | | ├──04_Soft-Margin_Support_Vector_Machine
    | | ├──05_Kernel_Logistic_Regression
    | | ├──06_Support_Vector_Regression
    | | ├──07_Blending_and_Bagging
    | | ├──08_Adaptive_Boosting
    | | ├──09_Decision_Tree
    | | ├──10_Random_Forest
    | | ├──11_Gradient_Boosted_Decision_Tree
    | | ├──12_Neural_Network
    | | ├──13_Deep_Learning
    | | ├──14_Radial_Basis_Function_Network
    | | ├──15_Matrix_Factorization
    | | └──16_Finale
    | ├──炼数成金-机器学习
    | | ├──第1课 机器学习概论
    | | ├──第2课 线性回归与Logistic。案例:电子商务业绩预测
    | | ├──第3课 岭回归,Lasso,变量选择技术。案例:凯撒密码破译
    | | ├──资料
    | | ├──机器学习第10周.rar 323.21M
    | | ├──机器学习第11周.rar 361.67M
    | | ├──机器学习第4周.rar 297.31M
    | | ├──机器学习第5周.rar 223.17M
    | | ├──机器学习第6周.rar 209.76M
    | | ├──机器学习第7周.rar 338.60M
    | | ├──机器学习第8周.rar 369.85M
    | | ├──机器学习第9周.rar 393.67M
    | | └──解压密码.TXT 0.05kb
    | ├──龙星计划_机器学
    | | ├──Lecture01(更多视频资料关注微信公众号【菜鸟要飞】).mp4 239.28M
    | | ├──Lecture02(更多视频资料关注微信公众号【菜鸟要飞】).mp4 222.03M
    | | ├──Lecture03(更多视频资料关注微信公众号【菜鸟要飞】).mp4 243.43M
    | | ├──Lecture04(更多视频资料关注微信公众号【菜鸟要飞】).mp4 255.50M
    | | ├──Lecture05(更多视频资料关注微信公众号【菜鸟要飞】).mp4 232.25M
    | | ├──Lecture06(更多视频资料关注微信公众号【菜鸟要飞】).mp4 135.64M
    | | ├──Lecture07(更多视频资料关注微信公众号【菜鸟要飞】).mp4 252.20M
    | | ├──Lecture08(更多视频资料关注微信公众号【菜鸟要飞】).mp4 209.73M
    | | ├──Lecture09(更多视频资料关注微信公众号【菜鸟要飞】).mp4 227.57M
    | | ├──Lecture10(更多视频资料关注微信公众号【菜鸟要飞】).mp4 281.58M
    | | ├──Lecture11(更多视频资料关注微信公众号【菜鸟要飞】).mp4 207.82M
    | | ├──Lecture12(更多视频资料关注微信公众号【菜鸟要飞】).mp4 237.86M
    | | ├──Lecture13(更多视频资料关注微信公众号【菜鸟要飞】).mp4 249.06M
    | | ├──Lecture14(更多视频资料关注微信公众号【菜鸟要飞】).mp4 213.10M
    | | ├──Lecture15(更多视频资料关注微信公众号【菜鸟要飞】).mp4 221.45M
    | | ├──Lecture16(更多视频资料关注微信公众号【菜鸟要飞】).mp4 248.63M
    | | ├──Lecture17(更多视频资料关注微信公众号【菜鸟要飞】).mp4 201.45M
    | | ├──Lecture18(更多视频资料关注微信公众号【菜鸟要飞】).mp4 220.02M
    | | ├──Lecture19_r(更多视频资料关注微信公众号【菜鸟要飞】).mp4 247.59M
    | | └──下载之前必看!更多视频资料下载目录.docx 479.29kb
    | ├──模式识别_35_国防科学技术大学(蔡宣平)
    | | ├──01.概述.flv 78.64M
    | | ├──02.特征矢量及特征空间、随机矢量、正态分布特性.flv 80.52M
    | | ├──03.聚类分析的概念、相似性测度.flv 83.17M
    | | ├──04.相似性测度(二).flv 85.84M
    | | ├──05.类间距离、准则函数.flv 75.86M
    | | ├──06.聚类算法:简单聚类算法、谱系聚类算法.flv 87.05M
    | | ├──07.聚类算法:动态聚类算法——C均值聚类算法.flv 66.62M
    | | ├──08.聚类算法:动态聚类算法——近邻函数算法.flv 88.00M
    | | ├──09.聚类算法实验.flv 12.93M
    | | ├──10.判别域界面方程分类的概念、线性判别函数.flv 66.89M
    | | ├──11.判别函数值的鉴别意义、权空间及解空间、fisher线性判别.flv 94.27M
    | | ├──12.线性可分条件下判别函数权矢量算法.flv 95.82M
    | | ├──13.一般情况下的判别函数权矢量算法.flv 75.44M
    | | ├──14.非线性判别函数.flv 110.17M
    | | ├──15.最近邻方法.flv 81.42M
    | | ├──16.感知器算法实验.flv 11.15M
    | | ├──17.最小误判概率准则.flv 78.78M
    | | ├──18.正态分布的最小误判概率、最小损失准则判决.flv 95.78M
    | | ├──19.含拒绝判决的最小损失准则、最小最大损失准则.flv 86.22M
    | | ├──20.Neyman—Pearson判决、实例.flv 73.79M
    | | ├──21.概述、矩法估计、最大似然估计.flv 80.00M
    | | ├──22.贝叶斯估计.flv 74.45M
    | | ├──23.贝叶斯学习.flv 91.37M
    | | ├──24.概密的窗函数估计方法.flv 106.70M
    | | ├──25.有限项正交函数级数逼近法.flv 83.89M
    | | ├──26.错误率估计.flv 62.26M
    | | ├──27.小结.flv 73.23M
    | | ├──28.实验3-4-5 Bayes分类器-kNN分类器-视频动目标检测.flv 72.58M
    | | ├──29.概述、类别可分性判据(一).flv 90.60M
    | | ├──30.类别可分性判据(二).flv 89.36M
    | | ├──31.基于可分性判据的特征提取.flv 99.45M
    | | ├──32.离散KL变换与特征提取.flv 66.85M
    | | ├──33.离散KL变换在特征提取与选择中的应用.flv 66.84M
    | | ├──34.特征选择中的直接挑选法.flv 57.54M
    | | └──35.综合实验-图像中的字符识别.flv 84.92M
    | ├──统计机器学习_41_上海交大(张志华)
    | | ├──01 概率基础.mp4 224.96M
    | | ├──02 随机变量1.mp4 222.51M
    | | ├──03 随机变量2.mp4 233.79M
    | | ├──04 高斯分布.mp4 218.95M
    | | ├──05 高斯分布例子.mp4 224.05M
    | | ├──06 连续分布.mp4 205.03M
    | | ├──07 jeffrey prior.mp4 213.12M
    | | ├──08 scale mixture pisribarin.mp4 371.55M
    | | ├──09 statistic interence.mp4 188.20M
    | | ├──10 Laplace 变换.mp4 237.59M
    | | ├──11 多元分布定义.mp4 185.37M
    | | ├──12 概率变换.mp4 180.62M
    | | ├──13 Jacobian.mp4 178.49M
    | | ├──14 Wedge production.mp4 180.58M
    | | ├──15 Wishart 分布.mp4 202.04M
    | | ├──16 多元正态分布.mp4 202.97M
    | | ├──17 统计量.mp4 197.62M
    | | ├──18 矩阵元Beta分布.mp4 76.21M
    | | ├──19 共轭先验性质.mp4 111.30M
    | | ├──20 统计量 充分统计量.mp4 210.65M
    | | ├──21 指数值分布.mp4 195.03M
    | | ├──22 Entropy.mp4 223.79M
    | | ├──23 KL distance.mp4 198.10M
    | | ├──24 Properties.mp4 125.99M
    | | ├──25 概率不等式1.mp4 225.13M
    | | ├──26 概率不等式2.mp4 188.98M
    | | ├──27 概率不等式1.mp4 206.29M
    | | ├──28 概率不等式2.mp4 183.60M
    | | ├──29 概率不等式3.mp4 187.71M
    | | ├──30 John 引理.mp4 145.53M
    | | ├──31 概率不等式.mp4 200.86M
    | | ├──32 随机投影.mp4 195.56M
    | | ├──33 Stochastic Convergence-概念.mp4 225.51M
    | | ├──34 Stochastic Convergence-性质.mp4 146.08M
    | | ├──35 Stochastic Convergence-应用.mp4 125.94M
    | | ├──36 EM算法1.mp4 229.42M
    | | ├──37 EM算法2.mp4 206.49M
    | | ├──38 EM算法3.mp4 142.07M
    | | ├──39 Bayesian Classification.mp4 201.56M
    | | ├──40 Markov Chain Monte carlo1.mp4 232.90M
    | | └──41 Markov Chain Monte carlo2.mp4 104.90M
    | └──南京大学周志华老师的一个讲普适机器学习的ppt【精品-ppt】.ppt 939.50kb
    ├──OpenCV技术
    | ├──01 OpenCV图像处理视频课程
    | | ├──01-概述 – OpenCV介绍与环境搭建.ts 120.84M
    | | ├──02-加载、修改、保存图像.ts 97.06M
    | | ├──03-矩阵的掩膜操作.ts 151.57M
    | | ├──04-Mat对象.ts 144.23M
    | | ├──05-图像操作.ts 107.17M
    | | ├──06-图像混合.ts 83.67M
    | | ├──07-调整图像亮度与对比度.ts 113.99M
    | | ├──08-绘制形状与文字.ts 170.69M
    | | ├──09-模糊图像一.ts 119.81M
    | | ├──10-图像模糊二.ts 143.37M
    | | ├──11-膨胀与腐蚀.ts 118.47M
    | | ├──12-形态学操作.ts 106.50M
    | | ├──13-形态学操作应用-提取水平与垂直线.ts 139.91M
    | | ├──14-图像金字塔-上采样与降采样.ts 121.70M
    | | ├──15-基本阈值操作.ts 137.90M
    | | ├──16-自定义线性滤波.ts 152.77M
    | | ├──17-处理边缘.ts 101.22M
    | | ├──18-Sobel算子.ts 176.98M
    | | ├──19-Laplance算子.ts 64.12M
    | | ├──20-Canny边缘检测.ts 140.30M
    | | ├──21-霍夫变换-直线.ts 113.02M
    | | ├──22-霍夫圆变换.ts 101.89M
    | | ├──23-像素重映射(cv__remap).ts 126.47M
    | | ├──24-直方图均衡化.ts 83.05M
    | | ├──25-直方图计算.ts 118.23M
    | | ├──26-直方图比较.ts 159.69M
    | | ├──27-直方图反向投影(Back Projection).ts 177.24M
    | | ├──28-模板匹配(Template Match).ts 157.27M
    | | ├──29-轮廓发现.ts 134.07M
    | | ├──30-凸包-Convex Hull.ts 140.75M
    | | ├──31-轮廓周围绘制矩形框和圆形框.ts 146.36M
    | | ├──32-图像矩(Image Moments).ts 158.37M
    | | ├──33-点多边形测试.ts 124.18M
    | | ├──34-基于距离变换与分水岭的图像分割-01.ts 169.62M
    | | ├──35-基于距离变换与分水岭的图像分割-02.ts 115.84M
    | | ├──课程配套PPT.zip 32.73M
    | | └──课程配套源代码.zip 5.84M
    | ├──02 OpenCV特征提取与检测实战视频课程
    | | ├──01-概述.ts 30.04M
    | | ├──02-OpenCV3.1.0编译.ts 120.21M
    | | ├──03-Harris角点检测-01.ts 84.48M
    | | ├──04-Harris角点检测-02.ts 89.03M
    | | ├──05-Shi-Tomasi角点检测.ts 130.39M
    | | ├──06-自定义角点检测器-01.ts 128.99M
    | | ├──07-自定义角点检测器-02.ts 87.50M
    | | ├──08-亚像素级别角点检测.ts 118.15M
    | | ├──09-SURF特征检测-01.ts 83.19M
    | | ├──10-SURF特征检测-02.ts 79.26M
    | | ├──11-SIFT特征检测-01.ts 104.13M
    | | ├──12-SIFT特征检测-02.ts 61.02M
    | | ├──13-HOG特征检测-01.ts 71.92M
    | | ├──14-HOG特征检测-02.ts 97.23M
    | | ├──15-LBP(Local Binary Patterns)特征-01.ts 75.54M
    | | ├──16-LBP(Local Binary Patterns)特征-02.ts 71.34M
    | | ├──17-LBP(Local Binary Patterns)特征-03.ts 150.87M
    | | ├──18-积分图计算.ts 68.93M
    | | ├──19-Haar特征.ts 63.62M
    | | ├──20-特征描述子.ts 71.90M
    | | ├──21-FLANN特征匹配.ts 93.87M
    | | ├──22-平面对象识别.ts 129.22M
    | | ├──23-AKAZE局部匹配-01.ts 84.27M
    | | ├──24-AKAZE局部匹配-02.ts 108.48M
    | | ├──25-Brisk特征检测与匹配.ts 102.78M
    | | ├──26-级联分类器 – 人脸检测.ts 103.23M
    | | ├──课程配套PDF.zip 11.28M
    | | └──课程配套源代码.zip 13.53kb
    | ├──03 OpenCV图像处理-小案例实战
    | | ├──01-概述.ts 35.41M
    | | ├──02-案例一 切边-01.ts 83.47M
    | | ├──03-案例一 切边-02.ts 86.61M
    | | ├──04-案例一 切边-03.ts 125.36M
    | | ├──05-案例二 直线检测-01.ts 108.18M
    | | ├──06-案例二 直线检测-02.ts 104.47M
    | | ├──07-案例三 对象提取-01.ts 97.74M
    | | ├──08-案例三 对象提取-02.ts 146.31M
    | | ├──09-案例四 对象计数-01.ts 106.72M
    | | ├──10-案例四 对象计数-02.ts 118.08M
    | | ├──11-案例五 透视校正-01.ts 125.45M
    | | ├──12-案例五 透视校正-02.ts 104.11M
    | | ├──13-案例五 透视校正-03.ts 139.17M
    | | ├──14-案例五 透视校正-04.ts 81.22M
    | | ├──15-案例六 对象提取与测量.ts 129.95M
    | | ├──课程配套PDF.zip 4.02M
    | | └──课程配套源代码.zip 7.46kb
    | ├──04 OpenCV级联分类器训练与使用实战教程课程
    | | ├──01-概述.ts 31.20M
    | | ├──02-Haar与LBP级联分类器原理介绍-01.ts 128.70M
    | | ├──03-Haar与LBP级联分类器原理介绍-02.ts 105.45M
    | | ├──04-Haar与LBP级联分类器使用-01.ts 154.83M
    | | ├──05-Haar与LBP级联分类器使用-02.ts 73.51M
    | | ├──06-HAAR猫脸检测.ts 124.13M
    | | ├──07-视频中人脸检测与眼睛跟踪-01.ts 157.92M
    | | ├──08-视频中人脸检测与眼睛跟踪-02.ts 118.79M
    | | ├──09-视频中人脸检测与眼睛跟踪-03.ts 115.74M
    | | ├──10-HAAR级联数据文件结构与精简.ts 118.48M
    | | ├──11-HAAR_LBP级联分类器训练-01.ts 142.52M
    | | ├──12-HAAR_LBP级联分类器训练-02.ts 98.63M
    | | ├──13-HAAR_LBP级联分类器训练-03.ts 120.12M
    | | ├──课程配套PDF.zip 3.17M
    | | └──课程配套源代码.zip 40.30M
    | ├──05 OpenCV图像分割实战视频教程
    | | ├──01-概述.ts 41.16M
    | | ├──02-KMeans方法-原理.ts 78.77M
    | | ├──03-KMeans方法-数据聚类.ts 80.89M
    | | ├──04-KMeans方法-图像分割.ts 99.61M
    | | ├──05-高斯混合模型(GMM)方法-原理与数据聚类.ts 138.96M
    | | ├──06-高斯混合模型(GMM)方法-图像分割.ts 130.83M
    | | ├──07-分水岭分割方法-原理.ts 82.94M
    | | ├──08-分水岭分割方法-对象分离与计数01.ts 131.68M
    | | ├──09-分水岭分割方法-对象分离与计数02.ts 110.41M
    | | ├──10-分水岭分割方法-图像分割.ts 120.66M
    | | ├──11-Grabcut原理与演示应用-原理.ts 110.45M
    | | ├──12-Grabcut原理与演示应用-代码演示.ts 113.80M
    | | ├──13-案例实战一证件照背景替换-01.ts 72.32M
    | | ├──14-案例实战一证件照背景替换.ts 124.49M
    | | ├──15-案例实战一绿幕背景视频抠图-01.ts 97.76M
    | | ├──16-案例实战一绿幕背景视频抠图.ts 94.69M
    | | ├──课程配套PDF.zip 3.05M
    | | └──课程配套代码与图片.zip 10.65M
    | ├──06 OpenCV视频分析与对象跟踪实战教程
    | | ├──01-概述.ts 113.81M
    | | ├──02-视频读写-01.ts 61.43M
    | | ├──03-视频读写-02.ts 111.93M
    | | ├──04-背景消除建模(BSM)-01.ts 84.93M
    | | ├──05-背景消除建模(BSM)-02.ts 99.72M
    | | ├──06-对象检测与跟踪(基于颜色)-01.ts 88.81M
    | | ├──07-对象检测与跟踪(基于颜色)-02.ts 78.98M
    | | ├──08-光流的对象跟踪-01.ts 134.66M
    | | ├──09-光流的对象跟踪-02.ts 58.51M
    | | ├──10-光流的对象跟踪-03.ts 137.56M
    | | ├──11-光流的对象跟踪-04.ts 115.82M
    | | ├──12-CAMShift对象跟踪.ts 112.90M
    | | ├──13-CAMShift对象跟踪-02.ts 62.82M
    | | ├──14-CAMShift对象跟踪-03.ts 123.35M
    | | ├──15-CAMShift对象跟踪-04.ts 143.86M
    | | ├──16-视频中移动对象统计.ts 138.98M
    | | ├──17-扩展模块中的跟踪方法介绍.ts 77.53M
    | | ├──18-扩展模块中的多对象跟踪.ts 93.33M
    | | ├──课程配套课件.zip 10.94M
    | | └──课程配套源代码.zip 54.99M
    | ├──07 OpenCV3.3深度神经网络(DNN)模块-应用视频教程
    | | ├──01-DNN模块概述.ts 92.36M
    | | ├──02-使用GoogleNet模型实现图像分类-01.ts 117.58M
    | | ├──03-使用GoogleNet模型实现图像分类-02.ts 95.08M
    | | ├──04-使用SSD模型实现对象检测-01.ts 124.48M
    | | ├──05-使用SSD模型实现对象检测-02.ts 140.36M
    | | ├──06-MobileNet模型实时对象检测.ts 110.08M
    | | ├──07-FCN模型实现图像分割-01.ts 102.94M
    | | ├──08-FCN模型图像分割-02.ts 100.21M
    | | ├──09-CNN模型预测性别与年龄.ts 146.81M
    | | ├──10-GOTURN模型实现视频对象跟踪.ts 142.50M
    | | ├──课程配套PDF.zip 2.20M
    | | └──课程配套源代码.zip 20.39M
    | ├──08 人工智能之OpenCV人脸识别案例实战视频教程
    | | ├──01-概述与环境准备.ts 47.74M
    | | ├──02-均值方差与协方差 协方差矩阵.ts 121.89M
    | | ├──03-特征值与特征向量.ts 90.70M
    | | ├──04-PCA原理与应用-01.ts 105.07M
    | | ├──05-PCA原理与应用-02.ts 161.12M
    | | ├──06-人脸识别算法之EigenFace-01.ts 137.73M
    | | ├──07-人脸识别算法之EigenFace-02.ts 134.85M
    | | ├──08-人脸识别算法之FisherFace.ts 100.25M
    | | ├──09-人脸识别算法之LBPH.ts 92.34M
    | | ├──10-案例-实时人脸识别应用开发-01.ts 120.61M
    | | ├──11-案例-实时人脸识别应用开发-02.ts 136.41M
    | | ├──课程配套PDF.zip 2.54M
    | | └──课程配套源代码.zip 3.63M
    | └──附赠:Opencv书籍
    | | ├──A_Computational_Approach_to_Edge_Detection-sz4.pdf 6.34M
    | | ├──Computer and Machine Vision Theory Algorithms Practicalities.pdf 22.17M
    | | ├──Learning OpenCV 2nd Early Release.pdf 10.95M
    | | ├──Mastering OpenCV with Practical Computer Vision Projects [eBook].pdf 6.33M
    | | ├──OpenCV.2.Computer.Vision.Application.Programming.Cookbook.pdf 6.78M
    | | ├──OpenCV2ComputerVisionApplicationProgrammingCookbookCode.zip 116.73kb
    | | ├──opencv2计算机视觉编程手册( 扫描版1-35页).pdf 68.34M
    | | ├──opencv2手册第五章.pdf 51.73M
    | | ├──OpenCV的计算机视觉技术实现.rar 13.56M
    | | ├──OpenCV教程基础篇-于仕琪-北航.pdf 23.79M
    | | ├──opencv手册.chm 2.58M
    | | ├──[数字图像处理与机器视觉:Visual.C++.与Matlab实现].张铮.扫描版.pdf 50.57M
    | | ├──机器视觉-张广军.pdf 48.75M
    | | ├──机器视觉测量技术.pdf 2.81M
    | | ├──机器视觉算法与应用.pdf 109.24M
    | | ├──基于OpenCV的计算机视觉技术实现.pdf 186.08M
    | | ├──计算机视觉——算法与应用.pdf.pdf 48.36M
    | | ├──计算机视觉(马颂德、张正友).pdf 13.60M
    | | ├──计算机视觉:算法与应用(Richard Szeliski-2010).pdf 22.14M
    | | ├──视觉计算理论.pdf 21.34M
    | | ├──数字图像处理与机器视觉――Visual C++与Matlab….iso 78.25M
    | | ├──图像处理、分析与机器视觉(第三版).pdf 76.50M
    | | ├──图像处理、分析与机器视觉(第三版)英文版.pdf 27.97M
    | | ├──图像处理分析与机器视觉(第二版)中译.pdf 40.99M
    | | ├──图像处理技术手册.pdf 145.15M
    | | ├──图像处理与计算机视觉算法及应用 原书第2版 [(美)帕科尔著][清华大学出版社][2012.05][388页]sample.pdf 6.20M
    | | ├──学习OpenCV 中文版.pdf 58.80M
    | | └──学习opencv书——源代码.zip 20.23M
    └──两个TensorFlow教程
    | ├──12-python视频 神经网络 Tensorflow 模块 视频教程 (带源码)
    | | ├──tensorflowTUT源码
    | | ├──Tensorflow 1 why .mp4 3.10M
    | | ├──Tensorflow 10 添加层.mp4 30.87M
    | | ├──Tensorflow 11 建造神经网络.mp4 64.79M
    | | ├──Tensorflow 12 结果可视化.mp4 22.43M
    | | ├──Tensorflow 13 优化器.mp4 16.42M
    | | ├──Tensorflow 14 可视化好帮手1.mp4 58.67M
    | | ├──Tensorflow 15 可视化好帮手2.mp4 26.36M
    | | ├──Tensorflow 16 分类学习.mp4 34.92M
    | | ├──Tensorflow 17 dropout 解决overfitting 问题.mp4 42.12M
    | | ├──Tensorflow 18-1 CNN卷积神经网络1.mp4 28.63M
    | | ├──Tensorflow 18-2 CNN卷积神经网络2.mp4 53.21M
    | | ├──Tensorflow 18-3 CNN卷积神经网络3.mp4 30.84M
    | | ├──Tensorflow 19 Saver保存读取.mp4 57.98M
    | | ├──Tensorflow 2 安装 (Windows, Mac, Linux).mp4 12.80M
    | | ├──Tensorflow 20.1 RNN 循环神经网络.mp4 28.26M
    | | ├──Tensorflow 20.2 RNN 循环神经网络 (分类例子).mp4 53.20M
    | | ├──Tensorflow 20.3 RNN lstm (regression 回归例子).mp4 59.24M
    | | ├──Tensorflow 20.4 RNN lstm (回归例子可视化).mp4 13.85M
    | | ├──Tensorflow 21 Autoencoder (非监督学习).mp4 29.31M
    | | ├──Tensorflow 22 scope 命名方式.mp4 40.96M
    | | ├──Tensorflow 23 Batch normalization 批标准化.mp4 37.01M
    | | ├──Tensorflow 3 例子1.mp4 6.07M
    | | ├──Tensorflow 4 处理结构.mp4 8.71M
    | | ├──Tensorflow 5 例子2.mp4 30.38M
    | | ├──Tensorflow 6 Session会话.mp4 12.97M
    | | ├──Tensorflow 7 变量.mp4 13.15M
    | | ├──Tensorflow 8 传入值.mp4 9.98M
    | | ├──Tensorflow 9 激励函数.mp4 34.16M
    | | └──TensorFlow 官方文档中文版 – v1.2.pdf 7.07M
    | └──Tensorflow源码级技术分享集(1)
    | | ├──Tensorflow源码级技术分享【第10期】.mp4 182.78M
    | | ├──Tensorflow源码级技术分享【第1期】.mp4 828.96M
    | | ├──Tensorflow源码级技术分享【第2期】.mp4 1023.80M
    | | ├──Tensorflow源码级技术分享【第3期】.mp4 703.70M
    | | ├──Tensorflow源码级技术分享【第4期】.mp4 983.37M
    | | ├──Tensorflow源码级技术分享【第5期】.mp4 604.22M
    | | ├──Tensorflow源码级技术分享【第6期】.mp4 1.41G
    | | ├──Tensorflow源码级技术分享【第7期】.mp4 1.16G
    | | ├──Tensorflow源码级技术分享【第8期】.mp4 497.54M
    | | └──Tensorflow源码级技术分享【第9期】.mp4 234.78M

     

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