├── README.md ├── practices ├── 01classification │ ├── models_caffe │ │ ├── ResNet │ │ │ ├── ResNet-101-deploy.prototxt │ │ │ ├── ResNet-152-deploy.prototxt │ │ │ └── ResNet-50-deploy.prototxt │ │ ├── alexnet_owt │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── bvlc_alexnet │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── bvlc_alexnet_fp16 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── bvlc_googlenet │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── bvlc_googlenet_fp16 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── bvlc_googlenet_v2 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── inception_v1 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── inception_v1_fp16 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── inception_v3 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── inception_v3_fp16 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── resnet_50 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── vgg_a │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── vgg_d │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ ├── vgg_d_fp16 │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ │ └── vgg_e │ │ │ ├── solver_tmpl.prototxt │ │ │ └── train_val_tmpl.prototxt │ └── readme.txt ├── 02segmentation │ ├── Semantic_Segmentation_PASCAL_VOC_2012.pdf │ ├── example_seg │ │ ├── 2007_002281.jpg │ │ ├── 2007_002281_seg.png │ │ ├── 2007_002619.jpg │ │ ├── 2007_002619_seg.png │ │ ├── 2007_005428.jpg │ │ ├── 2007_005428_seg.png │ │ ├── 2007_005845.jpg │ │ ├── 2007_005845_seg.png │ │ ├── 2007_007890.jpg │ │ ├── 2007_007890_seg.png │ │ ├── 2007_009779.jpg │ │ └── 2007_009779_seg.png │ ├── fcn_alexnet.prototxt │ ├── net_surgery.py │ └── pascal_prepare_dataset.sh ├── 03object_detection │ ├── examples │ │ ├── 002152.png │ │ ├── 002152.txt │ │ ├── 2012_001003.jpg │ │ └── 2012_001003.xml │ └── practice_Object_Detect.pdf ├── 04rnn │ └── readme.txt ├── 05whale_sound │ ├── example_whale_sound │ │ ├── train1.aiff │ │ ├── train1.png │ │ ├── train10.aiff │ │ ├── train10.png │ │ ├── train11.aiff │ │ ├── train11.png │ │ ├── train12.aiff │ │ ├── train12.png │ │ ├── train2.aiff │ │ ├── train2.png │ │ ├── train3.aiff │ │ ├── train3.png │ │ ├── train4.aiff │ │ ├── train4.png │ │ ├── train5.aiff │ │ ├── train5.png │ │ ├── train6.aiff │ │ ├── train6.png │ │ ├── train7.aiff │ │ ├── train7.png │ │ ├── train8.aiff │ │ ├── train8.png │ │ ├── train9.aiff │ │ └── train9.png │ ├── labels.txt │ ├── practice_whale_sound.pdf │ ├── preprocessWorks.py │ ├── run.sh │ ├── step1_train.csv │ ├── step3_train.csv │ ├── train.txt │ └── validate.txt ├── docker_launch_script.sh └── readme.txt └── slides ├── 1-1. Introduction to Machine Learning.pdf ├── 1-2. Neural Network for Machine Learning.pdf ├── 1-3. Convolutional Neural Network.pdf ├── 2-1. Convolutional Neural Network Models.pdf ├── 2-2. More NN for ML.pdf ├── 2-3. Advanced CNN.pdf ├── 3-1. Learning with Memory.pdf ├── 3-2. Practical Deep Learning.pdf └── 3-3. 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