├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── automl_in_action ├── 1_autokeras │ └── 1_simple_mnist.py ├── 2_nni │ ├── 1_mnist_before.py │ ├── 2_mnist.py │ ├── README.md │ ├── config.yml │ └── search_space.json ├── 3_adanet │ ├── 1_simple_boston.py │ └── 2_simple_mnist.py ├── 4_tfos │ ├── 1_mnist_data_setup.py │ ├── 1_start_spark.sh │ ├── 2_mnist_preprocess.sh │ ├── 2_mnist_spark.py │ ├── 3_mnist_spark_adanet.py │ ├── 3_mnist_train.sh │ ├── 4_mnist_inference.sh │ ├── 5_mnist_adanet_train.sh │ ├── README.md │ └── mnist_dist.py ├── 5_tfos_adanet │ ├── 1_start_spark.sh │ ├── 2_mac_local_run.sh │ ├── parse_result.py │ └── scripts │ │ └── train │ │ ├── adactr.py │ │ ├── adactr_dist.py │ │ └── tfspark.zip ├── 6_adanet_ctr │ ├── 1_adanet_criteo.py │ ├── 2_adanet_avazu.py │ ├── 3_dataset_test.py │ └── parse_result.py └── README.md ├── beauty_of_deep_learning ├── 1-simple-linear-regression.ipynb ├── 2-k-nearest-neighbor.ipynb └── 6_perceptron.py ├── core_python ├── 1_regular_expression │ ├── 1_basic_sample.py │ ├── 2_rewho.py │ └── README.md ├── 2_network │ ├── 1_tsTserv.py │ ├── 2_tsTclnt.py │ ├── 3_tsUserv.py │ ├── 4_tsUclnt.py │ ├── 5_tsTservSS.py │ ├── 6_tsTclntSS.py │ ├── 7_tsTservTW.py │ ├── 8_tsTclntTW.py │ └── README.md ├── 3_internet │ ├── 1_ftp_client.py │ ├── 2_nntp_client.py │ ├── 3_mail_client.py │ └── 4_generate_email.py ├── 4_multi_thread │ ├── 1_one_thread.py │ ├── 2_thread_wayA.py │ ├── 3_thread_wayB.py │ ├── 4_thread_fac_fib.py │ ├── 5_bookrank.py │ ├── 6_thread_wayC.py │ ├── 7_candy.py │ ├── 8_prodcons.py │ ├── README.md │ └── myThread.py ├── 5_gui │ ├── 1_tklabel.py │ ├── 2_tkbutton.py │ ├── 3_tklabel_and_btn.py │ ├── 4_tk_complex.py │ └── README.md ├── 6_web │ ├── 1_myhttpd.py │ └── README.md └── README.md ├── cs20 ├── 10_w2v_graph.py ├── 11_w2v_visual.py ├── 12_variable_no_sharing.py ├── 13_variable_sharing.py ├── 14_fully_connected.py ├── 15_randomization.py ├── 16_basic_kernels.py ├── 17_conv_mnist.py ├── 18_basic_tfrecord.py ├── 19_char_rnn.py ├── 1_basic_operation.py ├── 2_basic_constant.py ├── 3_basic_data_type.py ├── 4_basic_variable.py ├── 5_basic_linreg.py ├── 6_basic_linreg_ds.py ├── 7_basic_linreg_mnist.py ├── 8_eager_execution.py ├── 9_w2v_eager.py ├── README.md ├── kernels.py ├── sample │ └── birth_life_2010.txt ├── utils.py └── w2v_utils.py ├── cs230 ├── 1_vectorization_camparison.py ├── 2_linear_regression.py ├── 3_logistic_regression.py ├── 4_multi_classification.py ├── 5_nueral_network.py ├── 6_bias_variance.py ├── 7_svm.py ├── 8_kmeans_pca.py ├── 9_anomaly_and_rec.py ├── README.md ├── data.zip └── ui.py ├── d2l ├── 10_weight_decay_torch.py ├── 11_dropout_raw.py ├── 12_dropout_torch.py ├── 13_house_price.py ├── 14_mlp_module.py ├── 15_model_parameter.py ├── 16_custom_layer.py ├── 17_save_and_load.py ├── 18_conv2d.py ├── 19_channel.py ├── 1_tensor_basic.py ├── 20_lenet.py ├── 21_alexnet.py ├── 22_vgg.py ├── 23_nin.py ├── 24_googlenet.py ├── 25_batch_normalization_raw.py ├── 26_batch_normalization_torch.py ├── 27_resnet.py ├── 28_densenet.py ├── 29_jay_corpus.py ├── 2_vector_speed.py ├── 30_series_sampling.py ├── 31_char_rnn_raw.py ├── 32_char_rnn_torch.py ├── 33_gru_raw.py ├── 34_gru_torch.py ├── 35_lstm_raw.py ├── 36_lstm_torch.py ├── 37_local_minimum_and_saddle_point.py ├── 38_gradient_descent.py ├── 39_sgd_raw.py ├── 3_linear_regression_raw.py ├── 40_sgd_torch.py ├── 41_momentum.py ├── 42_adagrad.py ├── 43_rmsprop.py ├── 44_adadelta.py ├── 45_adam.py ├── 46_image_augmentation.py ├── 47_aug_cifar10.py ├── 48_fine_tune_hotdog.py ├── 49_word2vec.py ├── 4_linear_regression_torch.py ├── 50_similar_words.py ├── 51_rnn_sentiment.py ├── 52_cnn_sentiment.py ├── 53_machine_translation.py ├── 5_softmax_regression_raw.py ├── 6_softmax_regression_torch.py ├── 7_mlp_raw.py ├── 8_mlp_torch.py ├── 9_weight_decay_raw.py ├── README.md └── utils.py ├── data_science_master ├── 1_linear_model │ ├── 1_linear_basic.py │ └── 2_linear_statsmodels.py ├── 2_logistic_regression │ └── 1_explore_data.py └── 3_gradient │ ├── 1_tf_basic.py │ ├── 2_tf_linear.py │ └── logs │ └── gradient_descent │ └── events.out.tfevents.1564124043.wangdadeMacBook-Pro.local ├── deep_learning_from_scratch ├── 10_two_layer_net.py ├── 11_naive_optimizer_compare.py ├── 12_mnist_optimizer_compare.py ├── 13_weight_init_activation_histogram.py ├── 14_weight_init_compare.py ├── 15_batch_norm_test.py ├── 16_overfit_weight_decay.py ├── 17_overfit_dropout.py ├── 18_hyperparameter_optimization.py ├── 19_cnn.py ├── 1_numpy_basis.py ├── 20_show_filter.py ├── 2_matplotlib_basis.py ├── 3_perceptron.py ├── 4_activation_function.py ├── 5_matrix.py ├── 6_nn_basis.py ├── 7_gradient.py ├── 8_simple_net.py ├── 9_two_layer_net_naive.py ├── common │ ├── functions.py │ ├── gradient.py │ ├── layers.py │ ├── multi_layer_net.py │ ├── multi_layer_net_extend.py │ ├── optimizer.py │ ├── simple_cnn.py │ ├── trainer.py │ └── util.py └── dataset.py ├── deep_learning_intro ├── 1_basic_linear.py ├── 3_tensorboard_advance.py ├── 3_tensorboard_basic.py ├── 3_tensorboard_event.py └── 4_simulate_sin.py ├── fintech └── 01_abc_card.py ├── leetcode_golang ├── 1.go ├── 104.go ├── 11.go ├── 121.go ├── 122.go ├── 125.go ├── 134.go ├── 136.go ├── 14.go ├── 141.go ├── 142.go ├── 144.go ├── 15.go ├── 16.go ├── 160.go ├── 169.go ├── 19.go ├── 2.go ├── 20.go ├── 206.go ├── 21.go ├── 217.go ├── 226.go ├── 231.go ├── 235.go ├── 237.go ├── 240.go ├── 26.go ├── 28.go ├── 292.go ├── 3.go ├── 344.go ├── 4.go ├── 43.go ├── 5.go ├── 53.go ├── 557.go ├── 7.go ├── 70.go ├── 704.go ├── 8.go ├── 88.go └── 9.go ├── mlds ├── 1-numpy │ ├── 1_basic_ops.py │ ├── 2_sort_slice.py │ ├── 3_copy_broadcast.py │ └── 4_numpy_100.py ├── 2-pandas │ └── 1_basic_ops.py └── 3-scipy │ └── 1_basic_op.py ├── nlp_projects └── chatbot │ ├── 01_nltk_download.py │ ├── 02_basic_nltk.py │ ├── 03_cn_token.py │ ├── 04_scrapy_spider.py │ ├── 05_search_bot.py │ ├── 06_word2vec.py │ ├── 07_bert.py │ └── README.md ├── phe_test ├── README.md ├── local_test.py ├── spark_test.job ├── spark_test.py └── spark_test.sh ├── proba_lab ├── naruto.py └── pauc_test.py ├── python_deep_learning ├── 1_imdb_binary_classification.py ├── 2_reuters_multi_classification.py ├── 3_price_prediction.py ├── 4_cnn_mnist.py ├── 5_small_dataset_cnn │ ├── 1_prepare_data.py │ ├── 2_train_model.py │ ├── 3_vgg_feature_extraction_without_data_aug.py │ ├── 4_vgg_feature_extraction_with_data_aug.py │ ├── 5_vgg_fine_tuning.py │ ├── 6_visualize_cnn.py │ ├── 7_visualize_filters.py │ ├── 8_visualize_class_activation_map.py │ └── README.md ├── 6_nlp_rnn │ ├── 1_one_hot_sample.py │ ├── 2_word_2_embedding.py │ ├── 3_numpy_rnn.py │ ├── 4_imdb_simple_rnn.py │ ├── 5_imdb_lstm.py │ ├── 6_explore_jena_dataset.py │ ├── 7_predict_temperature.py │ └── 8_imdb_cnn.py ├── 7_advance_practice │ ├── 1_simple_functional_sample.py │ ├── 2_two_input_model.py │ ├── 3_three_output_model.py │ └── 4_keras_tensorboard.py └── 8_generate_learning │ ├── 1_generate_text.py │ ├── 2_deepdream.py │ ├── 3_nerual_style_transfer.py │ └── utils.py ├── tensorflow_in_action ├── 01-最简单的例子.ipynb ├── 02-定义不同的计算图.ipynb ├── 03-指定显卡.ipynb ├── 04-完整的神经网络样例.ipynb ├── 05-集合计算L2正则化.ipynb ├── 06-滑动平均模型.ipynb ├── 07-MNIST数据集使用.ipynb ├── keras │ ├── lenet.py │ └── sentiment_analysis.py ├── mnist │ ├── basic_model.py │ ├── lenet_mnist_eval.py │ ├── lenet_mnist_inference.py │ ├── lenet_mnist_train.py │ ├── mnist_eval.py │ ├── mnist_inference.py │ └── mnist_train.py ├── nlp │ ├── 1_ptb_gen_dict.py │ ├── 2_ptb_gen_idword.py │ ├── 3_ptb_train.py │ └── softmax_2_way.py ├── rnn │ └── simulate_sin.py ├── tensorboard │ ├── mnist_eval.py │ ├── mnist_histogram.py │ ├── mnist_inference.py │ ├── mnist_projector_generate.py │ ├── mnist_projector_show.py │ └── mnist_train.py ├── tfrecord_queue_and_dataset │ ├── coordinator_example.py │ ├── generate_mnist_tfrecord.py │ ├── load_mnist_tfrecord.py │ └── queuerunner_example.py └── transfer_learning │ ├── preprocessing.py │ └── train.py ├── 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