├── MLcoding ├── 00 天池大赛 │ ├── Code │ │ ├── .ipynb_checkpoints │ │ │ └── o2o-1-checkpoint.ipynb │ │ ├── 1_model.pkl │ │ ├── data │ │ │ ├── ccf_offline_stage1_test_revised.csv │ │ │ └── sample_submission.csv │ │ ├── o2o-1.ipynb │ │ ├── o2o_test.py │ │ └── submit1.csv │ ├── ccf_offline_stage1_test_revised.csv │ └── sample_submission.csv ├── 01 knn │ ├── datingTestSet2.txt │ ├── task1.py │ └── task2.py ├── 02 pla │ ├── data1.csv │ ├── data2.csv │ ├── demo_PLA.py │ ├── demo_PLA_np_hard.py │ └── 台湾大学林轩田机器学习基石课程学习笔记2+--+Learning+to+Answer+Yes-No.pdf ├── 03 Bayes │ ├── 03 Bayes.zip │ ├── demo_bayes.py │ ├── demo_bayes_gabage.py │ ├── demo_bayes_gabage_email.py │ └── email.zip ├── 04 kdtree │ ├── __pycache__ │ │ └── decisionTreePlot.cpython-36.pyc │ ├── decisionTreePlot.py │ ├── demo_kdtree.py │ ├── demo_kdtree_complete.py │ └── lenses.txt ├── 05 logistic │ ├── TestSet.txt │ ├── demo_logi.py │ ├── horseColicTest.txt │ └── horseColicTraining.txt ├── 06 svm │ ├── testSet.txt │ ├── testSetRBF.txt │ ├── testSetRBF2.txt │ ├── test_simple_svm.py │ └── test_svm.py ├── 07 adaboost │ ├── TestSet.txt │ ├── adaboost--sklearn.html │ ├── horseColicTest.txt │ ├── horseColicTraining.txt │ ├── test.ipynb │ └── test.py ├── 08 regress │ ├── KNN1.png │ ├── KNN2.png │ ├── abalone.txt │ ├── demo_regression.py │ ├── ex0.txt │ ├── ex1.txt │ ├── lstm_stock.py │ ├── regression.py │ ├── softmax1.png │ ├── softmax2.png │ ├── 微信截图_20181106231413.png │ ├── 微信截图_20181106233505.png │ └── 微信截图_20181106233520.png ├── 09 regKree │ ├── 01.png │ ├── 剪枝.png │ ├── 构建树.png │ └── 构建树2.png ├── 10 kmeans │ ├── Portland.png │ ├── kMeans.py │ ├── places.txt │ ├── portlandClubs.txt │ ├── testSet.txt │ ├── testSet2.txt │ ├── 微信截图_20181111000558.png │ └── 微信截图_20181111000851.png ├── 11 Apriori │ ├── README.md │ ├── apriori.py │ └── mushroom.dat ├── 12 fpGrowth │ ├── README.md │ ├── fpGrowth.py │ └── kosarak.dat ├── 13 PCA │ ├── pca.py │ ├── secom.data │ └── testSet.txt └── 14 SVD │ ├── 0_5.txt │ └── SVD.py ├── MSTAR_ATR ├── MSTAR2JPG.m ├── ReadME ├── data.rar ├── sar_recog.py ├── seg_data.m └── seg_data_demo.m ├── Pnet ├── main.py ├── omniglot.rar ├── preprocessing.py ├── prototypical-net.ipynb ├── prototypicalNet.py └── readme ├── README.md ├── cs231n ├── assignment1 │ ├── .ipynb_checkpoints │ │ ├── features-checkpoint.ipynb │ │ ├── knn-checkpoint.ipynb │ │ ├── softmax-checkpoint.ipynb │ │ ├── svm-checkpoint.ipynb │ │ └── two_layer_net-checkpoint.ipynb │ ├── README.md │ ├── collectSubmission.sh │ ├── cs231n │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── data_utils.cpython-36.pyc │ │ │ └── gradient_check.cpython-36.pyc │ │ ├── classifiers │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── k_nearest_neighbor.cpython-36.pyc │ │ │ │ ├── linear_classifier.cpython-36.pyc │ │ │ │ ├── linear_svm.cpython-36.pyc │ │ │ │ ├── neural_net.cpython-36.pyc │ │ │ │ └── softmax.cpython-36.pyc │ │ │ ├── k_nearest_neighbor.py │ │ │ ├── linear_classifier.py │ │ │ ├── linear_svm.py │ │ │ ├── neural_net.py │ │ │ └── softmax.py │ │ ├── data_utils.py │ │ ├── datasets │ │ │ └── get_datasets.sh │ │ ├── features.py │ │ ├── gradient_check.py │ │ └── vis_utils.py │ ├── features.ipynb │ ├── frameworkpython.txt │ ├── knn.ipynb │ ├── requirements.txt │ ├── setup_googlecloud.sh │ ├── softmax.ipynb │ ├── softmax.py │ ├── start_ipython_osx.sh │ ├── svm.ipynb │ └── two_layer_net.ipynb ├── assignment2 │ ├── BatchNormalization.ipynb │ ├── ConvolutionalNetworks.ipynb │ ├── Dropout.ipynb │ ├── FullyConnectedNets.ipynb │ ├── PyTorch.ipynb │ ├── README.md │ ├── TensorFlow.ipynb │ ├── collectSubmission.sh │ ├── cs231n │ │ ├── __init__.py │ │ ├── classifiers │ │ │ ├── __init__.py │ │ │ ├── cnn.py │ │ │ └── fc_net.py │ │ ├── data_utils.py │ │ ├── datasets │ │ │ └── get_datasets.sh │ │ ├── fast_layers.py │ │ ├── gradient_check.py │ │ ├── im2col.py │ │ ├── im2col_cython.pyx │ │ ├── layer_utils.py │ │ ├── layers.py │ │ ├── optim.py │ │ ├── setup.py │ │ ├── solver.py │ │ └── vis_utils.py │ ├── frameworkpython.txt │ ├── kitten.jpg │ ├── normalization.png │ ├── puppy.jpg │ ├── requirements.txt │ └── start_ipython_osx.sh └── assignment3 │ ├── GANs-PyTorch.ipynb │ ├── GANs-TensorFlow.ipynb │ ├── LSTM_Captioning.ipynb │ ├── NetworkVisualization-PyTorch.ipynb │ ├── NetworkVisualization-TensorFlow.ipynb │ ├── RNN_Captioning.ipynb │ ├── StyleTransfer-PyTorch.ipynb │ ├── StyleTransfer-TensorFlow.ipynb │ ├── collectSubmission.sh │ ├── cs231n │ ├── __init__.py │ ├── captioning_solver.py │ ├── classifiers │ │ ├── __init__.py │ │ ├── rnn.py │ │ └── squeezenet.py │ ├── coco_utils.py │ ├── data_utils.py │ ├── datasets │ │ ├── get_assignment3_data.sh │ │ ├── get_coco_captioning.sh │ │ ├── get_imagenet_val.sh │ │ └── get_squeezenet_tf.sh │ ├── fast_layers.py │ ├── gradient_check.py │ ├── im2col.py │ ├── im2col_cython.pyx │ ├── image_utils.py │ ├── layer_utils.py │ ├── layers.py │ ├── optim.py │ ├── rnn_layers.py │ └── setup.py │ ├── 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