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.DS_Store
.DS_Store
 
 
README.md
README.md
 
 
RandDel.java
RandDel.java
 
 
convolutional_mlp.py
convolutional_mlp.py
 
 
data_fm.py
data_fm.py
 
 
dl_utils.py
dl_utils.py
 
 
drop_mlp3fm.py
drop_mlp3fm.py
 
 
drop_mlp4da.py
drop_mlp4da.py
 
 
drop_mlp4daloop.py
drop_mlp4daloop.py
 
 
mlp.py
mlp.py
 
 
rbm.py
rbm.py
 
 
ucl_avg_ctr.py
ucl_avg_ctr.py
 
 
ucl_denosing_autoencoder.py
ucl_denosing_autoencoder.py
 
 
ucl_ensamble_models.py
ucl_ensamble_models.py
 
 
ucl_ensamble_models_by_weights.py
ucl_ensamble_models_by_weights.py
 
 
ucl_fm.py
ucl_fm.py
 
 
ucl_gaussian_binary_rbm.py
ucl_gaussian_binary_rbm.py
 
 
ucl_gaussian_binary_rbm_sparse.py
ucl_gaussian_binary_rbm_sparse.py
 
 
ucl_logistic_minibatch_fm.py
ucl_logistic_minibatch_fm.py
 
 
ucl_lr.py
ucl_lr.py
 
 
ucl_mlp.py
ucl_mlp.py
 
 
ucl_mlp3da.py
ucl_mlp3da.py
 
 
ucl_mlp3fm.py
ucl_mlp3fm.py
 
 
ucl_mlp4da.py
ucl_mlp4da.py
 
 
ucl_mlp_3.py
ucl_mlp_3.py
 
 
ucl_mlp_keras.py
ucl_mlp_keras.py
 
 
ucl_mlp_keras_init_weights.py
ucl_mlp_keras_init_weights.py
 
 
ucl_mlp_keras_init_weights_manaul_test.py
ucl_mlp_keras_init_weights_manaul_test.py
 
 
ucl_mlp_sparse.py
ucl_mlp_sparse.py
 
 
ucl_mlp_sparse_3.py
ucl_mlp_sparse_3.py
 
 
ucl_mlp_sparse_3_autoencoder.py
ucl_mlp_sparse_3_autoencoder.py
 
 
ucl_mlp_sparse_3_rbm.py
ucl_mlp_sparse_3_rbm.py
 
 
ucl_mlp_sparse_3_rbm_nosave.py
ucl_mlp_sparse_3_rbm_nosave.py
 
 
ucl_mlp_sparse_3_rbm_sparseinput.py
ucl_mlp_sparse_3_rbm_sparseinput.py
 
 
ucl_mlp_sparse_4.py
ucl_mlp_sparse_4.py
 
 
ucl_mlp_sparse_4_autoencoder.py
ucl_mlp_sparse_4_autoencoder.py
 
 
ucl_mlp_sparse_7.py
ucl_mlp_sparse_7.py
 
 
utils.py
utils.py
 
 

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deep_learning

all codes are based on Python+Theano.

there are two deep neural network models in this repository. 1)Factorization Machine + neural networks 2)Spase inputs with neural networks with sampled initlization

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Python+Theano for Ad CTR prediction

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