深度学习框架Caffe学习与应用视频教程 炼数成金深度学习技术 Caffe视频教程
深度学习框架Caffe学习与应用视频教程 炼数成金深度学习技术 Caffe视频教程https://bbs.cniaoba.com/uploads/14031602566629.jpg
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===============课程目录===============
│├
││├第一课.pptx
││├
│││├Linux下OpenCV安装.pptx
│││├linux下安装.mov
│││├源码>
││││├ippicv_linux_20151201.tgz
││││├opencv.zip
││││├opencv_contrib.zip
││││└opencv-3.1.0.exe
│││├
││││├gcc_command.docx
││││├lena.jpg
││││└test_opencv.cpp
││├
│││├part1_课程介绍.mov
│││├part2_深度学习介绍.mov
│││├part3_caffe介绍.mov
│││├part4_caffe安装.mov
│││└part5_作业.mov
││├
│││├Deep Learning (Bengio 2015-10-03).pdf
│││├DeepLearning-NowPublishing-Vol7-SIG-039.pdf
│││├master.zip
│││├Understanding Machine Learning - From Theory to Algorithms.pdf
│││├神经网络与机器学习(第3版）.pdf
│││└神经网络与深度学习讲义20151211.pdf
│├
││├第二课.pptx
││├
│││├
││││├part1_前言.mov
││││├part2_代码目录结构.mov
││││├part3_blob源码分析.mov
││││├part4_blob编程操作.mov
││││├part5_layer&Net.mov
││││└part6_proto介绍和编码使用.mov
││├
│││├part1_前言.mov
│││├part2_代码目录结构.mov
│││├part3_blob源码分析.mov
│││├part4_blob编程操作.mov
│││├part5_layer&Net.mov
│││├part6_proto介绍和编码使用.mov
│││└part7_牛刀小试mnist数据集.mov
││├素材>
│││└dataguru.class.proto
│├
││├第三课.pptx
││├
││├
│││├caffe_lecture3_part1_前言.mp4
│││├caffe_lecture3_part2_solver介绍.mp4
│││├caffe_lecture3_part3_solver参数配置与优化方法.mp4
│││├caffe_lecture3_part4_io模块介绍.mp4
│││├caffe_lecture3_part5_图片转换lmdb.mp4
│││└caffe_lecture3_part6_使用训练好的模型.mp4
││├
│││├ RMSProp_Divide the gradient by a running average of its recent magnitude.pdf
│││├A Practical Guide to Training Restricted Boltzmann Machines.pdf
│││├ADADELTA AN ADAPTIVE LEARNING RATE METHOD.pdf
│││├ADAM_A METHOD FOR STOCHASTIC OPTIMIZATION.pdf
│││├Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.pdf
│││├On the importance of initialization and momentum in deep learning.pdf
│││└Readme.txt
│├
││├第三课的勘误.pdf
││├第四课.pptx
││├
││├
│││├caffe_lecture4_part1_前言.mp4
│││├caffe_lecture4_part2_可视化工具.mp4.zip
│││└caffe_lecture4_part3_卷积、池化、全连接、激活和Softmax.mp4
│├
││├第五课.pptx
││├
││├
│││├caffe_lecture5_part1_前言.mp4
│││├caffe_lecture5_part2_1_自定义Layer计算层.mp4
│││├caffe_lecture5_part2_2_自定义Layer计算层.mp4
│││└caffe_lecture5_part3_自定义数据输入层.mp4
││├
│││└digits.png
│├
││├【参考教程】vim打造C++ IDE.pdf
││├第六课.pptx
││├
│││├my_solver.cpp
│││├my_solver.hpp
│││├
││││├caffe.proto
││││├digits.png
││││├my_data_layer.cpp
││││├my_data_layer.hpp
││││├mydata_lenet_solver.prototxt
││││└mydata_lenet_train_test.prototxt
││├
│││├caffe_lecture6_part1_上周作业讲解(自定义数据层).mp4.zip
│││├caffe_lecture6_part2_自定义损失层与softmax讲解.mp4.zip
│││└caffe_lecture6_part3_自定义solver.mp4
│├
││├【补充】虚拟机镜像.txt
││├第七课.pptx
││├
│││├Faster R-CNN.pdf
│││├Girshick_Fast_R-CNN_ICCV_2015_paper.pdf
│││├Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf
│││├README.png
│││├SPPNet.pdf
│││├SSD.pdf
│││└YOLO.pdf
││├
│││├caffe_lecture7_part1_RCNN_SPPNET.mp4.zip
│││├caffe_lecture7_part2_FRCNN_YOLO_SSD.mp4.zip
│││└caffe_lecture7_part3_pythonlayer.mp4.zip
│├
││├第八课.pptx
││├
│││├caffe_lecture8_part1_矩阵运算.mp4.zip
│││└caffe_lecture8_part2_Caffe最小化.mp4.zip
│├
││├第九课.pptx
││├
││├
│││├caffe_lecture9_part1.mp4.zip
│││├caffe_lecture9_part2.mp4.zip
│││└caffe_lecture9_part3.mp4.zip
│├
││├第十课.pptx
││├
│││├4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
│││├Delving Deep into Rectifiers- Surpassing Human-Level Performance on ImageNet Classification.pdf
│││├Dropout- A Simple Way to Prevent Neural Networks from Overfitting.pdf
│││└t502v.Neural.Networks.Tricks.of.the.Trade.pdf
││├
│││├caffe_lecture10_part1_前言.mp4.zip
│││├caffe_lecture10_part2_数据预处理tricks.mp4.zip
│││├caffe_lecture10_part3_训练tricks.mp4.zip
│││└caffe_lecture10_part4_可视化结果分析tricks_实战tricks.mp4.zip
││├
│││└101_ObjectCategories.tar.gz
│├
││├第十一课.pptx
││├
││├
│││├caffe_lecture11_part1.mkv
│││└caffe_lecture11_part2.mkv
││├
│││├neg.zip
│││└pos.txt
│├
││├第十二课.pptx
││├
││├
│││├caffe_lecture12_part1.mp4
│││├caffe_lecture12_part2.mp4
│││└caffe_lecture12_part3.mp4
│├
││├第十三课.pptx
││├
│││└caffe_lecture13_part1.mp4
││├
**** Hidden Message *****
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