MachineLN博客目录 忘是亡心i 2021-09-30 03:22 361阅读 0赞 MachineLN博客目录 [https://blog.csdn.net/u014365862/article/details/78422372][https_blog.csdn.net_u014365862_article_details_78422372] 本文为博主原创文章，未经博主允许不得转载。有问题可以加微信：lp9628(注明CSDN)。 公众号MachineLN，邀请您扫码关注： MachineLP的Github（欢迎follow）：[https://github.com/MachineLP][https_github.com_MachineLP] [train\_cnn\_v0][train_cnn_v0]： 实现基础cnn训练，数据读取方式慢。 [train\_cnn\_v1][train_cnn_v1]： 优化数据读取的方式，学习率加入衰减。 [train\_cnn-rnn][train_cnn-rnn]：在train\_cnn\_v0基础上加入rnn。 [train\_cnn-rnn-attention\_v0][train_cnn-rnn-attention_v0]：在train\_cnn\_v0基础上加入rnn、attention。 [train\_cnn\_multiGPU\_v0][train_cnn_multiGPU_v0]：使用多GPU训练(默认两块gpu)，以上其他框架使用多GPU，只需把train.py替换掉就可以了。 [train\_cnn\_multilabel][train_cnn_multilabel]： 多任务多标签训练及其总结。 [train\_cnn\_GANs][train_cnn_GANs]： GANs训练及其总结。 [TensorFlow基础教程][TensorFlow]：理论及其代码实践。 [python实践教程][python]：MachineLP的日常代码。 李宏毅老师：[http://speech.ee.ntu.edu.tw/~tlkagk/courses\_MLDS17.html][http_speech.ee.ntu.edu.tw_tlkagk_courses_MLDS17.html] sklearn：[http://scikit-learn.org/stable/modules/multiclass.html][http_scikit-learn.org_stable_modules_multiclass.html] keras文档：[https://keras.io/models/model/][https_keras.io_models_model] TF python API：[https://tensorflow.google.cn/api\_docs/python/][https_tensorflow.google.cn_api_docs_python] [Neural Networks and Deep Learning][]：[http://neuralnetworksanddeeplearning.com][http_neuralnetworksanddeeplearning.com] **机器学习进阶系列：(下面内容在微信公众号同步)** 1. [机器学习-1：MachineLN之三要素][-1_MachineLN] 2. [机器学习-2：MachineLN之模型评估][-2_MachineLN] 3. [机器学习-3：MachineLN之dl][-3_MachineLN_dl] 4. [机器学习-4：DeepLN之CNN解析][-4_DeepLN_CNN] 5. [机器学习-5：DeepLN之CNN权重更新（笔记）][-5_DeepLN_CNN] 6. [机器学习-6：DeepLN之CNN源码][-6_DeepLN_CNN] 7. [机器学习-7：MachineLN之激活函数][-7_MachineLN] 8. [机器学习-8：DeepLN之BN][-8_DeepLN_BN] 9. [机器学习-9：MachineLN之数据归一化][-9_MachineLN] 10. [机器学习-10：MachineLN之样本不均衡][-10_MachineLN] 11. [机器学习-11：MachineLN之过拟合][-11_MachineLN] 12. [机器学习-12：MachineLN之优化算法][-12_MachineLN] 13. [机器学习-13：MachineLN之kNN][-13_MachineLN_kNN] 14. [机器学习-14：MachineLN之kNN源码][-14_MachineLN_kNN] 15. [机器学习-15：MachineLN之感知机][-15_MachineLN] 16. [机器学习-16：MachineLN之感知机源码][-16_MachineLN] 17. [机器学习-17：MachineLN之逻辑回归][-17_MachineLN] 18. [机器学习-18：MachineLN之逻辑回归源码][-18_MachineLN] 19. [机器学习-19：MachineLN之SVM（1）][-19_MachineLN_SVM_1] 20. [机器学习-20：MachineLN之SVM（2）][-20_MachineLN_SVM_2] 21. [机器学习-21：MachineLN之SVM源码][-21_MachineLN_SVM] 22. [机器学习-22：MachineLN之RL][-22_MachineLN_RL] 23. [机器学习-23：MachineLN之朴素贝叶斯][-23_MachineLN] 24. [机器学习-24：MachineLN之朴素贝叶斯源码][-24_MachineLN] **人脸检测系列：** 1. [人脸检测——AFLW准备人脸][AFLW] 2. [人脸检测——生成矫正人脸——cascade cnn的思想， 但是mtcnn的效果貌似更赞][cascade cnn_ _mtcnn] 3. [人脸检测——准备非人脸][Link 1] 4. [人脸检测——矫正人脸生成标签][Link 2] 5. [人脸检测——mtcnn思想，生成negative、positive、part样本。][mtcnn_negative_positive_part] 6. [**人脸检测——****滑动窗口篇（训练和实现）**][Link 3] 7. [**人脸检测——fcn**][fcn] 8. [简单的人脸跟踪][Link 4] 9. [Face Detection(OpenCV) Using Hadoop Streaming API][Face Detection_OpenCV_ Using Hadoop Streaming API] 10. [Face Recognition(face\_recognition) Using Hadoop Streaming API][Face Recognition_face_recognition_ Using Hadoop Streaming API] 11. [非极大值抑制（Non-Maximum-Suppression）][Non-Maximum-Suppression] **OCR****系列：** **1. [tf20: CNN—识别字符验证码][tf20_ CNN]** 2. [**身份证识别——****生成身份证号和汉字**][Link 5] 3. [**tf21:** **身份证识别——****识别身份证号**][tf21_] 4. **[tf22: ocr识别——不定长数字串识别][tf22_ ocr]** **机器学习，深度学习系列:** 1. [反向传播与它的直观理解][Link 6] 2. [卷积神经网络（CNN）：从原理到实现][CNN] 3. [机器学习算法应用中常用技巧-1][-1] 4. [机器学习算法应用中常用技巧-2][-2] 5. [一个隐马尔科夫模型的应用实例：中文分词][Link 7] 6. [**Pandas****处理csv****表格**][Pandas_csv] 7. [sklearn查看数据分布][sklearn] 8. [TensorFlow 聊天机器人][TensorFlow 1] 9. [YOLO][] 10. [感知机--模型与策略][--] 11. [从 0 到 1 走进 Kaggle][0 _ 1 _ Kaggle] 12. [python调用Face++，玩坏了！][python_Face] 13. [人脸识别keras实现教程][keras] 14. [机器学习中的Bias(偏差)，Error(误差)，和Variance(方差)有什么区别和联系？][Bias_Error_Variance] 15. [CNN—pooling层的作用][CNN_pooling] 16. [trick—Batch Normalization][trick_Batch Normalization] 17. [**tensorflow****使用BN—Batch Normalization**][tensorflow_BN_Batch Normalization] 18. [trick—Data Augmentation][trick_Data Augmentation] 19. [CNN图图图][CNN 1] 20. [为什么很多做人脸的Paper会最后加入一个Local Connected Conv？][Paper_Local Connected Conv] 21. [**Faster RCNN****：RPN****，anchor****，sliding windows**][Faster RCNN_RPN_anchor_sliding windows] 22. [**深度学习这些坑你都遇到过吗？**][Link 8] 23. [**image——Data Augmentation****的代码**][image_Data Augmentation] 24. [8种常见机器学习算法比较][8] 25. [几种常见的激活函数][Link 9] 26. [**Building powerful image classification models using very little data**][Building powerful image classification models using very little data] 27. [**机器学习模型训练时候tricks**][tricks] 28. [OCR综述][OCR] 29. [一个有趣的周报][Link 10] 30. [根据已给字符数据，训练逻辑回归、随机森林、SVM，生成ROC和箱线][SVM_ROC] 31. [一个不错的教程][Link 11] 32. [matplotlib画廊][matplotlib] **图像处理系列：** 1. [python下使用cv2.drawContours填充轮廓颜色][python_cv2.drawContours] 2. [imge stitching图像拼接stitching][imge stitching_stitching] 3. [用python简单处理图片（1）：打开\\显示\\保存图像][python_1] 4. [用python简单处理图片（2）：图像通道\\几何变换\\裁剪][python_2] 5. [用python简单处理图片（3）：添加水印][python_3] 6. [用python简单处理图片（4）：图像中的像素访问][python_4] 7. [用python简单处理图片（5）：图像直方图][python_5] 8. [**仿射变换，透视变换：二维坐标到二维坐标之间的线性变换，可用于landmark****人脸矫正。**][landmark] **代码整合系列：** 1. [windows下C++如何调用matlab程序][windows_C_matlab] 2. [ubuntu下C++如何调用matlab程序][ubuntu_C_matlab] 3. [matlab使用TCP/IP Server Sockets][matlab_TCP_IP Server Sockets] 4. [ubuntu下C++如何调用python程序，gdb调试C++代码][ubuntu_C_python_gdb_C] 5. [How to pass an array from C++ to an embedded python][How to pass an array from C_ to an embedded python] 6. [如何使用Python为Hadoop编写一个简单的MapReduce程序][Python_Hadoop_MapReduce] 7. [图像的遍历][Link 12] 8. [**ubuntu****下CMake****编译生成动态库和静态库，以OpenTLD****为例。**][ubuntu_CMake_OpenTLD] 9. [**ubuntu****下make****编译生成动态库，然后python****调用cpp****。**][ubuntu_make_python_cpp] **数据结构和算法系列：** 1. [堆排序][Link 13] 2. [red and black (深度优先搜索算法dfs)][red and black _dfs] 3. [深度优先搜索算法][Link 14] 4. [qsort原理和实现][qsort] 5. [stack实现queue ; list实现stack][stack_queue _ list_stack] 6. [另一种斐波那契数列][Link 15] 7. [堆和栈的区别(个人感觉挺不错的)][Link 16] 8. [排序方法比较][Link 17] 9. [漫画 ：什么是红黑树？][Link 18] 10. [牛客网刷题][Link 19] 11. [莫烦python 666][python 666] 12. [paddlepaddle][] **kinect** **系列：** 1. [Kinect v2.0原理介绍之一：硬件结构][Kinect v2.0] 2. [Kinect v2.0原理介绍之二：6种数据源][Kinect v2.0_6] 3. [Kinect v2.0原理介绍之三：骨骼跟踪的原理][Kinect v2.0 1] 4. [Kinect v2.0原理介绍之四：人脸跟踪探讨][Kinect v2.0 2] 5. [Kinect v2.0原理介绍之五：只检测离kinect最近的人脸][Kinect v2.0_kinect] 6. [Kinect v2.0原理介绍之六：Kinect深度图与彩色图的坐标校准][Kinect v2.0_Kinect] 7. [Kinect v2.0原理介绍之七：彩色帧获取][Kinect v2.0 3] 8. [Kinect v2.0原理介绍之八：高清面部帧(1) FACS 介绍][Kinect v2.0_1_ FACS] 9. [Kinect v2.0原理介绍之九：高清面部帧(2) 面部特征对齐][Kinect v2.0_2_] 10. [Kinect v2.0原理介绍之十：获取高清面部帧的AU单元特征保存到文件][Kinect v2.0_AU] 11. [kinect v2.0原理介绍之十一：录制视频][kinect v2.0] 12. [Kinect v2.0原理介绍之十二：音频获取][Kinect v2.0 4] 13. [Kinect v2.0原理介绍之十三：面部帧获取][Kinect v2.0 5] 14. [Kinect for Windows V2和V1对比开发\_\_\_彩色数据获取并用OpenCV2.4.10显示][Kinect for Windows V2_V1_OpenCV2.4.10] 15. [Kinect for Windows V2和V1对比开发\_\_\_骨骼数据获取并用OpenCV2.4.10显示][Kinect for Windows V2_V1_OpenCV2.4.10 1] 16. [用kinect录视频库][kinect] **tensorflow****系列：** 1. [Ubuntu 16.04 安装 Tensorflow(GPU支持)][Ubuntu 16.04 _ Tensorflow_GPU] 2. [使用Python实现神经网络][Python] 3. [tf1: nn实现评论分类][tf1_ nn] 4. [tf2: nn和cnn实现评论分类][tf2_ nn_cnn] 5. [tf3: RNN—mnist识别][tf3_ RNN_mnist] 6. [tf4: CNN—mnist识别][tf4_ CNN_mnist] 7. [tf5: Deep Q Network—AI游戏][tf5_ Deep Q Network_AI] 8. [tf6: autoencoder—WiFi指纹的室内定位][tf6_ autoencoder_WiFi] 9. [tf7: RNN—古诗词][tf7_ RNN] 10. [tf8:RNN—生成音乐][tf8_RNN] 11. [tf9: PixelCNN][tf9_ PixelCNN] 12. [tf10: 谷歌Deep Dream][tf10_ _Deep Dream] 13. [tf11: retrain谷歌Inception模型][tf11_ retrain_Inception] 14. [tf12: 判断男声女声][tf12_] 15. [tf13: 简单聊天机器人][tf13_] 16. [tf14: 黑白图像上色][tf14_] 17. [tf15: 中文语音识别][tf15_] 18. [tf16: 脸部特征识别性别和年龄][tf16_] 19. [tf17: “声音大挪移”][tf17_] 20. [tf18: 根据姓名判断性别][tf18_] 21. [tf19: 预测铁路客运量][tf19_] 22. [**tf20: CNN—****识别字符验证码**][tf20_ CNN] 23. [tf21: 身份证识别——识别身份证号][tf21_] 24. [tf22: ocr识别——不定长数字串识别][tf22_ ocr] 25. [tf23: “恶作剧” --人脸检测][tf23_ _ --] 26. [tf24: GANs—生成明星脸][tf24_ GANs] 27. [tf25: 使用深度学习做阅读理解+完形填空][tf25_] 28. [tf26: AI操盘手][tf26_ AI] 29. [tensorflow\_cookbook--preface][tensorflow_cookbook--preface] 30. [01 TensorFlow入门（1）][01 TensorFlow_1] 31. [01 TensorFlow入门（2）][01 TensorFlow_2] 32. [02 The TensorFlow Way（1）][02 The TensorFlow Way_1] 33. [02 The TensorFlow Way（2）][02 The TensorFlow Way_2] 34. [02 The TensorFlow Way（3）][02 The TensorFlow Way_3] 35. [03 Linear Regression][] 36. [04 Support Vector Machines][] 37. [tf API 研读1：tf.nn，tf.layers， tf.contrib概述][tf API _1_tf.nn_tf.layers_ tf.contrib] 38. [tf API 研读2：math][tf API _2_math] 39. [tensorflow中的上采样(unpool)和反卷积(conv2d\_transpose)][tensorflow_unpool_conv2d_transpose] 40. [tf API 研读3：Building Graphs][tf API _3_Building Graphs] 41. [tf API 研读4：Inputs and Readers][tf API _4_Inputs and Readers] 42. [tf API 研读5：Data IO][tf API _5_Data IO] 43. [tf API 研读6：Running Graphs][tf API _6_Running Graphs] 44. [**tf.contrib.rnn.static\_rnn****与tf.nn.dynamic\_rnn****区别**][tf.contrib.rnn.static_rnn_tf.nn.dynamic_rnn] 45. [**Tensorflow****使用的预训练的resnet\_v2\_50****，resnet\_v2\_101****，resnet\_v2\_152****等模型预测，训练**][Tensorflow_resnet_v2_50_resnet_v2_101_resnet_v2_152] 46. [**tensorflow****下设置使用某一块GPU****、多GPU****、CPU****的情况**][tensorflow_GPU_GPU_CPU] 47. [**工业器件检测和识别**][Link 20] 48. [**将tf****训练的权重保存为CKPT,PB ,CKPT** **转换成 PB****格式。并将权重固化到图里面,****并使用该模型进行预测**][tf_CKPT_PB _CKPT_ _ PB] 49. **[tensorsor快速获取所有变量，和快速计算L2范数][tensorsor_L2]** 50. [**cnn+rnn+attention**][cnn_rnn_attention] 51. [Tensorflow实战学习笔记][TensorFlow] 52. [tf27: Deep Dream—应用到视频][tf27_ Deep Dream] 53. [tf28: 手写汉字识别][tf28_] 54. [tf29: 使用tensorboard可视化inception\_v4][tf29_ _tensorboard_inception_v4] 55. [tf30: center loss及其mnist上的应用][tf30_ center loss_mnist] 56. [tf31: keras的LSTM腾讯人数在线预测][tf31_ keras_LSTM] 57. [tf32: 一个简单的cnn模型：人脸特征点训练][tf32_ _cnn] 58. [tf33: 图片降噪：卷积自编码][tf33_] 59. [tf34：从ckpt中读取权重值][tf34_ckpt] 60. [tf35：tf.estimator][tf35_tf.estimator] 61. [tf36：使用tensorbord显示图片][tf36_tensorbord] 62. [tf37：tensorflow中将模型的权重值限定范围][tf37_tensorflow] 63. [tf38：tensorflow使用pipeline通过队列方式读取数据训练][tf38_tensorflow_pipeline] 64. [tf39：tensorflow之seq2seq][tf39_tensorflow_seq2seq] 65. [tf40：图像检索(triplet\_loss)之Conditional Similarity Networks][tf40_triplet_loss_Conditional Similarity Networks] 66. [tf41：使用TF models训练自己的目标检测器][tf41_TF models] 67. [tf42：tensorflow多GPU训练][tf42_tensorflow_GPU] 68. [tf43：tensorflow Serving gRPC 部署实例][tf43_tensorflow Serving gRPC] **torch****系列：** 1. [torch01：torch基础][torch01_torch] 2. [torch02：logistic regression--MNIST识别][torch02_logistic regression--MNIST] 3. [torch03：linear\_regression][torch03_linear_regression] 4. [torch04：全连接神经网络--MNIST识别和自己数据集][torch04_--MNIST] 5. [torch05：CNN--MNIST识别和自己数据集][torch05_CNN--MNIST] 6. [torch06：ResNet--Cifar识别和自己数据集][torch06_ResNet--Cifar] 7. [torch07：RNN--MNIST识别和自己数据集][torch07_RNN--MNIST] 8. [torch08：RNN--word\_language\_model][torch08_RNN--word_language_model] 9. [torch09：variational\_autoencoder(VAE)--MNIST和自己数据集][torch09_variational_autoencoder_VAE_--MNIST] **C++****系列：** 1. [c++ primer之const限定符][c_ primer_const] 2. [c++primer之auto类型说明符][c_primer_auto] 3. [c++primer之预处理器][c_primer] 4. [c++primer之string][c_primer_string] 5. [c++primer之vector][c_primer_vector] 6. [c++primer之多维数组][c_primer 1] 7. [c++primer之范围for循环][c_primer_for] 8. [c++primer之运算符优先级表][c_primer 2] 9. [c++primer之try语句块和异常处理][c_primer_try] 10. [c++primer之函数(函数基础和参数传递)][c_primer 3] 11. [c++primer之函数(返回类型和return语句)][c_primer_return] 12. [c++primer之函数重载][c_primer 4] 13. [c++重写卷积网络的前向计算过程，完美复现theano的测试结果][c_theano] 14. [c++ primer之类][c_ primer] 15. [c++primer之类（构造函数再探）][c_primer 5] 16. [c++primer之类（类的静态成员）][c_primer 6] 17. [c++primer之顺序容器（容器库概览）][c_primer 7] 18. [c++primer之顺序容器（添加元素）][c_primer 8] 19. [c++primer之顺序容器（访问元素）][c_primer 9] **OpenCV****系列：** 1. [自己训练SVM分类器，进行HOG行人检测。][SVM_HOG] 2. [opencv-haar-classifier-training][] 3. [vehicleDectection with Haar Cascades][] 4. [LaneDetection][] 5. [OpenCV学习笔记大集锦][OpenCV] 6. [Why always OpenCV Error: Assertion failed (elements\_read == 1) in unknown function ?][Why always OpenCV Error_ Assertion failed _elements_read _ 1_ in unknown function] 7. [目标检测之训练opencv自带的分类器（opencv\_haartraining 或 opencv\_traincascade）][opencv_opencv_haartraining _ opencv_traincascade] 8. [车牌识别 之 字符分割][Link 21] 9. **[仿射变换，透视变换：二维坐标到二维坐标之间的线性变换，可用于landmark人脸矫正。][landmark]** 10. [opencv实现抠图（单一背景），替换背景图][opencv] 11. [python下使用cv2.drawContours填充轮廓颜色][python_cv2.drawContours] 12. [使用openCV提取sift；surf；hog特征][openCV_sift_surf_hog] 13. [opencv--基于深度学习的人脸检测器][opencv--] **python****系列（web开发、多线程等）：** 1. [**flask****的web****开发，用于机器学习（主要还是DL****）模型的简单演示。**][flask_web_DL] 2. **[python多线程，获取多线程的返回值][python 1]** 3. [文件中字的统计及创建字典][Link 22] 4. [socket基础][socket] **其他:** 1. [MAC平台下Xcode配置使用OpenCV的具体方法 (2016最新)][MAC_Xcode_OpenCV_ _2016] 2. [**python****下如何安装.whl****包？**][python_.whl] 3. [给中国学生的第三封信：成功、自信、快乐][Link 23] 4. [自己-社会-机器学习][-_-] 5. [不执著才叫看破，不完美才叫人生。][Link 24] 6. [PCANet的C++代码——详细注释版][PCANet_C] 7. [责任与担当][Link 25] 8. [好走的都是下坡路][Link 26] 9. [一些零碎的语言，却触动到内心深处。][Link 27] 10. [用一个脚本学习 python][python 2] 11. [一个有趣的周报][Link 10] \*\*\* Ubuntu: [http://man.linuxde.net/download/Ubuntu][http_man.linuxde.net_download_Ubuntu] 转载于:https://www.cnblogs.com/DicksonJYL/p/9604482.html [https_blog.csdn.net_u014365862_article_details_78422372]: https://blog.csdn.net/u014365862/article/details/78422372 [https_github.com_MachineLP]: https://github.com/MachineLP [train_cnn_v0]: https://github.com/MachineLP/train_arch/tree/master/train_cnn_v0 [train_cnn_v1]: https://github.com/MachineLP/train_arch/tree/master/train_cnn_v1 [train_cnn-rnn]: https://github.com/MachineLP/train_cnn-rnn [train_cnn-rnn-attention_v0]: https://github.com/MachineLP/train_cnn-rnn-attention [train_cnn_multiGPU_v0]: https://github.com/MachineLP/train_arch/tree/master/train_cnn_multiGPU_v0 [train_cnn_multilabel]: https://github.com/MachineLP/train_cnn_multilabel [train_cnn_GANs]: https://github.com/MachineLP/train_cnn_GANs [TensorFlow]: https://github.com/MachineLP/Tensorflow- [python]: https://github.com/MachineLP/py_workSpace [http_speech.ee.ntu.edu.tw_tlkagk_courses_MLDS17.html]: http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS17.html [http_scikit-learn.org_stable_modules_multiclass.html]: http://scikit-learn.org/stable/modules/multiclass.html [https_keras.io_models_model]: https://keras.io/models/model/ [https_tensorflow.google.cn_api_docs_python]: https://tensorflow.google.cn/api_docs/python/ [Neural Networks and Deep Learning]: http://neuralnetworksanddeeplearning.com/index.html [http_neuralnetworksanddeeplearning.com]: http://neuralnetworksanddeeplearning.com/ [-1_MachineLN]: http://blog.csdn.net/u014365862/article/details/78955063 [-2_MachineLN]: http://blog.csdn.net/u014365862/article/details/78959353 [-3_MachineLN_dl]: http://blog.csdn.net/u014365862/article/details/78980142 [-4_DeepLN_CNN]: http://blog.csdn.net/u014365862/article/details/78986089 [-5_DeepLN_CNN]: http://blog.csdn.net/u014365862/article/details/78959211 [-6_DeepLN_CNN]: http://blog.csdn.net/u014365862/article/details/79010248 [-7_MachineLN]: http://blog.csdn.net/u014365862/article/details/79007801 [-8_DeepLN_BN]: http://blog.csdn.net/u014365862/article/details/79019518 [-9_MachineLN]: http://blog.csdn.net/u014365862/article/details/79031089 [-10_MachineLN]: http://blog.csdn.net/u014365862/article/details/79040390 [-11_MachineLN]: http://blog.csdn.net/u014365862/article/details/79057073 [-12_MachineLN]: http://blog.csdn.net/u014365862/article/details/79070721 [-13_MachineLN_kNN]: http://blog.csdn.net/u014365862/article/details/79091913 [-14_MachineLN_kNN]: http://blog.csdn.net/u014365862/article/details/79101209 [-15_MachineLN]: http://blog.csdn.net/u014365862/article/details/79135612 [-16_MachineLN]: http://blog.csdn.net/u014365862/article/details/79135767 [-17_MachineLN]: http://blog.csdn.net/u014365862/article/details/79157777 [-18_MachineLN]: http://blog.csdn.net/u014365862/article/details/79157841 [-19_MachineLN_SVM_1]: http://blog.csdn.net/u014365862/article/details/79184858 [-20_MachineLN_SVM_2]: http://blog.csdn.net/u014365862/article/details/79202089 [-21_MachineLN_SVM]: http://blog.csdn.net/u014365862/article/details/79224119 [-22_MachineLN_RL]: http://blog.csdn.net/u014365862/article/details/79240997 [-23_MachineLN]: http://blog.csdn.net/u014365862/article/details/79388289 [-24_MachineLN]: http://blog.csdn.net/u014365862/article/details/79388445 [AFLW]: http://blog.csdn.net/u014365862/article/details/74682464 [cascade cnn_ _mtcnn]: http://blog.csdn.net/u014365862/article/details/74690865 [Link 1]: http://blog.csdn.net/u014365862/article/details/74719498 [Link 2]: http://blog.csdn.net/u014365862/article/details/74853099 [mtcnn_negative_positive_part]: http://blog.csdn.net/u014365862/article/details/78051411 [Link 3]: http://blog.csdn.net/u014365862/article/details/77816493 [fcn]: http://blog.csdn.net/u014365862/article/details/78036382 [Link 4]: http://blog.csdn.net/u014365862/article/details/77989896 [Face Detection_OpenCV_ Using Hadoop Streaming API]: http://blog.csdn.net/u014365862/article/details/78173740 [Face Recognition_face_recognition_ Using Hadoop Streaming API]: http://blog.csdn.net/u014365862/article/details/78175803 [Non-Maximum-Suppression]: http://blog.csdn.net/u014365862/article/details/53376516 [tf20_ CNN]: http://blog.csdn.net/u014365862/article/details/53869816 [Link 5]: http://blog.csdn.net/u014365862/article/details/78581949 [tf21_]: http://blog.csdn.net/u014365862/article/details/78582128 [tf22_ ocr]: http://blog.csdn.net/u014365862/article/details/78582417 [Link 6]: http://blog.csdn.net/u014365862/article/details/54728707 [CNN]: http://blog.csdn.net/u014365862/article/details/54865609 [-1]: http://blog.csdn.net/u014365862/article/details/54890040 [-2]: http://blog.csdn.net/u014365862/article/details/54890046 [Link 7]: http://blog.csdn.net/u014365862/article/details/54891582 [Pandas_csv]: http://blog.csdn.net/u014365862/article/details/54923429 [sklearn]: http://blog.csdn.net/u014365862/article/details/54973495 [TensorFlow 1]: http://blog.csdn.net/u014365862/article/details/57518873 [YOLO]: http://blog.csdn.net/u014365862/article/details/60321879 [--]: http://blog.csdn.net/u014365862/article/details/61413859 [0 _ 1 _ Kaggle]: http://blog.csdn.net/u014365862/article/details/72794198 [python_Face]: http://blog.csdn.net/u014365862/article/details/74149097 [keras]: http://blog.csdn.net/u014365862/article/details/74332028 [Bias_Error_Variance]: http://blog.csdn.net/u014365862/article/details/76360351 [CNN_pooling]: http://blog.csdn.net/u014365862/article/details/77159143 [trick_Batch Normalization]: http://blog.csdn.net/u014365862/article/details/77159778 [tensorflow_BN_Batch Normalization]: http://blog.csdn.net/u014365862/article/details/77188011 [trick_Data Augmentation]: http://blog.csdn.net/u014365862/article/details/77193754 [CNN 1]: http://blog.csdn.net/u014365862/article/details/77367172 [Paper_Local Connected Conv]: http://blog.csdn.net/u014365862/article/details/77795902 [Faster RCNN_RPN_anchor_sliding windows]: http://blog.csdn.net/u014365862/article/details/77887230 [Link 8]: http://blog.csdn.net/u014365862/article/details/77961624 [image_Data Augmentation]: http://blog.csdn.net/u014365862/article/details/78086604 [8]: http://blog.csdn.net/u014365862/article/details/52937983 [Link 9]: http://blog.csdn.net/u014365862/article/details/52710698 [Building powerful image classification models using very little data]: http://blog.csdn.net/u014365862/article/details/78519629 [tricks]: http://blog.csdn.net/u014365862/article/details/78519727 [OCR]: https://handong1587.github.io/deep_learning/2015/10/09/ocr.html#handwritten-recognition [Link 10]: http://blog.csdn.net/u014365862/article/details/78757109 [SVM_ROC]: http://blog.csdn.net/u014365862/article/details/78835541 [Link 11]: https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/chapter1.html [matplotlib]: https://matplotlib.org/gallery.html [python_cv2.drawContours]: 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[Python_Hadoop_MapReduce]: http://blog.csdn.net/u014365862/article/details/78169554 [Link 12]: http://blog.csdn.net/u014365862/article/details/53513710 [ubuntu_CMake_OpenTLD]: http://blog.csdn.net/u014365862/article/details/78663269 [ubuntu_make_python_cpp]: http://blog.csdn.net/u014365862/article/details/78675033 [Link 13]: http://blog.csdn.net/u014365862/article/details/78200711 [red and black _dfs]: http://blog.csdn.net/u014365862/article/details/48781603 [Link 14]: http://blog.csdn.net/u014365862/article/details/48729681 [qsort]: http://blog.csdn.net/u014365862/article/details/48688457 [stack_queue _ list_stack]: http://blog.csdn.net/u014365862/article/details/48594323 [Link 15]: http://blog.csdn.net/u014365862/article/details/48573545 [Link 16]: http://blog.csdn.net/u014365862/article/details/49159499 [Link 17]: http://blog.csdn.net/u014365862/article/details/52502824 [Link 18]: https://mp.weixin.qq.com/s/JJVbi7kqDpLUuh696J7oLg [Link 19]: https://www.nowcoder.com/activity/oj [python 666]: https://morvanzhou.github.io/ [paddlepaddle]: http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.cn.html [Kinect v2.0]: http://blog.csdn.net/u014365862/article/details/46713807 [Kinect v2.0_6]: http://blog.csdn.net/u014365862/article/details/46849253 [Kinect v2.0 1]: http://blog.csdn.net/u014365862/article/details/46849309 [Kinect v2.0 2]: http://blog.csdn.net/u014365862/article/details/46849357 [Kinect v2.0_kinect]: http://blog.csdn.net/u014365862/article/details/47809401 [Kinect v2.0_Kinect]: http://blog.csdn.net/u014365862/article/details/48212085 [Kinect v2.0 3]: http://blog.csdn.net/u014365862/article/details/48212377 [Kinect v2.0_1_ FACS]: http://blog.csdn.net/u014365862/article/details/48212631 [Kinect v2.0_2_]: http://blog.csdn.net/u014365862/article/details/48212757 [Kinect v2.0_AU]: http://blog.csdn.net/u014365862/article/details/48780361 [kinect v2.0]: http://blog.csdn.net/u014365862/article/details/77929405 [Kinect v2.0 4]: http://blog.csdn.net/u014365862/article/details/49204931 [Kinect v2.0 5]: http://blog.csdn.net/u014365862/article/details/50434088 [Kinect for Windows V2_V1_OpenCV2.4.10]: http://blog.csdn.net/u014365862/article/details/48948861 [Kinect for Windows V2_V1_OpenCV2.4.10 1]: http://blog.csdn.net/u014365862/article/details/48949055 [kinect]: http://blog.csdn.net/u014365862/article/details/48946543 [Ubuntu 16.04 _ Tensorflow_GPU]: http://blog.csdn.net/u014365862/article/details/53868411 [Python]: http://blog.csdn.net/u014365862/article/details/53868414 [tf1_ nn]: http://blog.csdn.net/u014365862/article/details/53868418 [tf2_ nn_cnn]: http://blog.csdn.net/u014365862/article/details/53868422 [tf3_ RNN_mnist]: http://blog.csdn.net/u014365862/article/details/53868425 [tf4_ CNN_mnist]: http://blog.csdn.net/u014365862/article/details/53868430 [tf5_ Deep Q Network_AI]: http://blog.csdn.net/u014365862/article/details/53868436 [tf6_ autoencoder_WiFi]: 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http://blog.csdn.net/u014365862/article/details/78914151 [socket]: https://gist.github.com/kevinkindom/108ffd675cb9253f8f71 [MAC_Xcode_OpenCV_ _2016]: http://blog.csdn.net/u014365862/article/details/53067565 [python_.whl]: http://blog.csdn.net/u014365862/article/details/51817390 [Link 23]: http://blog.csdn.net/u014365862/article/details/47972321 [-_-]: http://blog.csdn.net/u014365862/article/details/48604145 [Link 24]: http://blog.csdn.net/u014365862/article/details/49079047 [PCANet_C]: http://blog.csdn.net/u014365862/article/details/51213280 [Link 25]: http://blog.csdn.net/u014365862/article/details/51841590 [Link 26]: http://blog.csdn.net/u014365862/article/details/53244402 [Link 27]: http://blog.csdn.net/u014365862/article/details/53186012 [python 2]: http://blog.csdn.net/u014365862/article/details/54428373 [http_man.linuxde.net_download_Ubuntu]: http://man.linuxde.net/download/Ubuntu
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