台大李宏毅 (Hung-Yi Lee) 教授的Machine Learning (2016, Fall)
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2021-01-01
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台大李宏毅 (Hung-Yi Lee) 教授的Machine Learning (2016, Fall) （119个子文件） 
check_index.csv 93.78MB sample_submission.csv 51.39MB train_data.csv 3.71MB spam_train.csv 615KB train.csv 455KB valid_data.csv 345KB test_X.csv 192KB spam_test.csv 91KB pseudo_inverse.csv 5KB gradient_descent.csv 5KB result1.csv 4KB result2.csv 4KB sampleSubmission.csv 4KB sampleSubmission.csv 2KB hw0_data.dat 34KB .flake8 248B .gitignore 1KB model2.h5 20KB MODEL_st-cnn_ycnet3_002.hdf5 11.26MB MODEL_spv-cnn_ycnet3_002.hdf5 11.26MB MODEL_ae-cnn_AutoencoderClassifier02_002.hdf5 11.26MB check_gen02_loss2.30.hdf5 142KB check_gen03_loss2.30.hdf5 142KB check_gen01_loss2.30.hdf5 142KB MODEL_ae-cnn_TestAEClassifier_001.hdf5 142KB check_round3_gen02_loss2.12.hdf5 86KB check_round3_gen01_loss2.13.hdf5 86KB MODEL_st-cnn_test_001.hdf5 86KB check_round2_gen01_loss2.16.hdf5 86KB check_round2_gen02_loss2.15.hdf5 86KB check_round1_gen01_loss2.18.hdf5 86KB check_round1_gen02_loss2.18.hdf5 86KB MODEL_spv-cnn_test_001.hdf5 86KB check_gen01_loss2.28.hdf5 86KB check_gen03_loss2.21.hdf5 86KB check_gen02_loss2.24.hdf5 86KB Pipfile.lock 37KB LOG_st-cnn_ycnet3_002.logg 4.76MB LOG_ae-cnn_AutoencoderClassifier02_002.logg 4.28MB LOG_spv-cnn_ycnet3_002.logg 918KB LOG_st-cnn_test_001.logg 201KB LOG_ae-cnn_TestAEClassifier_001.logg 124KB LOG_spv-cnn_test_001.logg 72KB README.md 8KB ae.model 12.32MB spambase.names 3KB corpus.p 18.14MB model1.p 1.44MB colors.p 38KB
instruction_hw3.pdf 582KB instruction_hw2.pdf 473KB Pipfile 510B
Lena.png 170KB ans2.png 148KB LOSS_st-cnn_ycnet3_002_round2.png 40KB LOSS_st-cnn_ycnet3_002_round19.png 37KB LOSS_st-cnn_ycnet3_002_round20.png 37KB autoencoder_01.png 36KB LOSS_st-cnn_ycnet3_002_round18.png 35KB LOSS_st-cnn_ycnet3_002_round7.png 35KB LOSS_st-cnn_ycnet3_002_round2.png 35KB LOSS_st-cnn_ycnet3_002_round3.png 34KB LOSS_st-cnn_ycnet3_002_round17.png 34KB LOSS_st-cnn_ycnet3_002_round6.png 33KB LOSS_st-cnn_ycnet3_002_round12.png 32KB LOSS_st-cnn_ycnet3_002_round14.png 31KB LOSS_st-cnn_ycnet3_002_round1.png 30KB LOSS_st-cnn_ycnet3_002_round15.png 30KB LOSS_st-cnn_ycnet3_002_round10.png 30KB LOSS_st-cnn_ycnet3_002_round1.png 30KB LOSS_spv-cnn_ycnet3_002.png 29KB LOSS_st-cnn_ycnet3_002_round5.png 28KB LOSS_ae-cnn_AutoencoderClassifier02_002.png 27KB LOSS_st-cnn_ycnet3_002_round3.png 27KB LOSS_spv-cnn_test_001.png 27KB LOSS_st-cnn_ycnet3_002_round16.png 26KB LOSS_st-cnn_ycnet3_002_round9.png 26KB LOSS_st-cnn_ycnet3_002_round13.png 26KB LOSS_st-cnn_ycnet3_002_round8.png 26KB pca_20.png 25KB LOSS_st-cnn_ycnet3_002_round4.png 25KB LOSS_st-cnn_ycnet3_002_round11.png 25KB LOSS_st-cnn_ycnet3_002_round8.png 24KB LOSS_st-cnn_test_001_round2.png 24KB LOSS_st-cnn_ycnet3_002_round4.png 24KB LOSS_st-cnn_ycnet3_002_round5.png 23KB LOSS_st-cnn_ycnet3_002_round9.png 23KB LOSS_st-cnn_test_001_round1.png 23KB LOSS_st-cnn_test_001_round3.png 22KB LOSS_st-cnn_ycnet3_002_round10.png 20KB LOSS_st-cnn_ycnet3_002_round7.png 18KB LOSS_ae-cnn_TestAEClassifier_001.png 18KB LOSS_st-cnn_ycnet3_002_round6.png 17KB model_configs.py 16KB train_AE.py 12KB ae_classifier_configs.py 10KB utility.py 10KB main.py 7KB self_train_cnn.py 7KB logistic_regression.py 6KB共 119 条
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