├── .gitignore ├── README.md ├── ab_testing ├── advertisement_clicks.csv ├── bayesian_bandit.py ├── bayesian_normal.py ├── bayesian_starter.py ├── cdfs_and_percentiles.py ├── chisquare.py ├── ci_comparison.py ├── client.py ├── comparing_epsilons.py ├── convergence.py ├── demo.py ├── epsilon_greedy.py ├── epsilon_greedy_starter.py ├── ex_chisq.py ├── ex_ttest.py ├── extra_reading.txt ├── optimistic.py ├── optimistic_starter.py ├── server_solution.py ├── server_starter.py ├── ttest.py ├── ucb1.py └── ucb1_starter.py ├── airline ├── ann.py ├── international-airline-passengers.csv ├── lr.py └── rnn.py ├── ann_class ├── backprop.py ├── extra_reading.txt ├── forwardprop.py ├── regression.py ├── sklearn_ann.py ├── tf_example.py └── xor_donut.py ├── ann_class2 ├── __init__.py ├── adam.py ├── batch_norm_tf.py ├── batch_norm_theano.py ├── cntk_example.py ├── dropout_tensorflow.py ├── dropout_theano.py ├── extra_reading.txt ├── grid_search.py ├── keras_example.py ├── keras_functional.py ├── mlp.py ├── momentum.py ├── mxnet_example.py ├── pytorch_batchnorm.py ├── pytorch_dropout.py ├── pytorch_example.py ├── pytorch_example2.py ├── random_search.py ├── rmsprop.py ├── rmsprop_test.py ├── sgd.py ├── tensorflow1.py ├── tensorflow2.py ├── tf_with_save.py ├── theano1.py ├── theano2.py ├── theano_ann.py └── util.py ├── ann_logistic_extra ├── __init__.py ├── ann_predict.py ├── ann_train.py ├── ecommerce_data.csv ├── logistic_predict.py ├── logistic_softmax_train.py ├── logistic_train.py └── process.py ├── bayesian_ml ├── 1 │ ├── Q.csv │ ├── README │ ├── Xtest.csv │ ├── Xtrain.csv │ ├── nb.py │ ├── ytest.csv │ └── ytrain.csv ├── 2 │ ├── Q.csv │ ├── README │ ├── Xtest.csv │ ├── Xtrain.csv │ ├── em.py │ ├── probit.py │ ├── ytest.csv │ └── ytrain.csv ├── 3 │ ├── X_set1.csv │ ├── X_set2.csv │ ├── X_set3.csv │ ├── run.py │ ├── y_set1.csv │ ├── y_set2.csv │ ├── y_set3.csv │ ├── z_set1.csv │ ├── z_set2.csv │ └── z_set3.csv └── 4 │ ├── data.txt │ ├── emgmm.py │ ├── npbgmm.py │ └── vigmm.py ├── best_fit_line.py ├── calculus ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── chatgpt_trading ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── cnn_class ├── WHERE ARE THE NOTEBOOKS.txt ├── benchmark.py ├── blur.py ├── cifar.py ├── cnn_tf.py ├── cnn_tf_plot_filters.py ├── cnn_theano.py ├── cnn_theano_plot_filters.py ├── custom_blur.py ├── echo.py ├── edge.py ├── edge_benchmark.py ├── exercises.txt ├── extra_reading.txt ├── helloworld.wav ├── keras_example.py └── lena.png ├── cnn_class2 ├── WHERE ARE THE NOTEBOOKS.txt ├── class_activation_maps.py ├── content │ ├── elephant.jpg │ └── sydney.jpg ├── extra_reading.txt ├── fashion.py ├── fashion2.py ├── make_limited_datasets.py ├── siamese.py ├── ssd.py ├── style_transfer1.py ├── style_transfer2.py ├── style_transfer3.py ├── styles │ ├── flowercarrier.jpg │ ├── lesdemoisellesdavignon.jpg │ ├── monalisa.jpg │ └── starrynight.jpg ├── test_softmax.py ├── tf_resnet.py ├── tf_resnet_convblock.py ├── tf_resnet_convblock_starter.py ├── tf_resnet_first_layers.py ├── tf_resnet_first_layers_starter.py ├── tf_resnet_identity_block.py ├── tf_resnet_identity_block_starter.py ├── use_pretrained_weights_resnet.py ├── use_pretrained_weights_vgg.py └── util.py ├── data_csv ├── X.txt ├── X_orig.txt ├── legend.txt ├── readme.txt └── y.txt ├── financial_engineering └── go_here_instead.txt ├── hmm_class ├── __init__.py ├── coin_data.txt ├── edgar_allan_poe.txt ├── extra_reading.txt ├── frost.py ├── generate_c.py ├── generate_ht.py ├── helloworld.wav ├── hmm_classifier.py ├── hmmc.py ├── hmmc_concat.py ├── hmmc_scaled_concat.py ├── hmmc_scaled_concat_diag.py ├── hmmc_tf.py ├── hmmc_theano.py ├── hmmc_theano2.py ├── hmmd.py ├── hmmd_scaled.py ├── hmmd_tf.py ├── hmmd_theano.py ├── hmmd_theano2.py ├── robert_frost.txt ├── scan1.py ├── scan2.py ├── scan3.py ├── site_data.csv ├── sites.py ├── tf_scan1.py ├── tf_scan2.py └── tf_scan3.py ├── keras_examples ├── ann.py ├── basic_mlp.py ├── batchnorm.py ├── cnn.py ├── cnn_cifar.py ├── cnn_dropout_batchnorm.py ├── dropout.py ├── sentiment_analysis.py ├── sine.py ├── sine2.py ├── translation.py └── util.py ├── kerascv └── imagenet_label_names.json ├── linear_algebra ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── linear_regression_class ├── data_1d.csv ├── data_2d.csv ├── data_poly.csv ├── gd.py ├── generate_1d.py ├── generate_2d.py ├── generate_poly.py ├── gradient_descent.py ├── l1_regularization.py ├── l2_regularization.py ├── lr_1d.py ├── lr_2d.py ├── lr_poly.py ├── mlr02.xls ├── moore.csv ├── moore.py ├── overfitting.py └── systolic.py ├── logistic_regression_class ├── bad_xor.py ├── l1_regularization.py ├── logistic1.py ├── logistic2.py ├── logistic3.py ├── logistic4.py ├── logistic_donut.py ├── logistic_visualize.py └── logistic_xor.py ├── mnist_csv ├── Q.txt ├── Xtest.txt ├── Xtrain.txt ├── label_test.txt └── label_train.txt ├── naive_bayes ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── nlp_class ├── all_book_titles.txt ├── article_spinner.py ├── cipher_placeholder.py ├── electronics │ ├── negative.review │ ├── positive.review │ └── unlabeled.review ├── extra_reading.txt ├── lsa.py ├── nb.py ├── sentiment.py ├── spam2.py ├── spambase.data └── stopwords.txt ├── nlp_class2 ├── bow_classifier.py ├── extra_reading.txt ├── glove.py ├── glove_svd.py ├── glove_tf.py ├── glove_theano.py ├── logistic.py ├── markov.py ├── ner.txt ├── ner_baseline.py ├── ner_rnn.py ├── ner_tf.py ├── neural_network.py ├── neural_network2.py ├── pmi.py ├── pos_baseline.py ├── pos_hmm.py ├── pos_ner_keras.py ├── pos_rnn.py ├── pos_tf.py ├── pretrained_glove.py ├── pretrained_w2v.py ├── recursive_tensorflow.py ├── recursive_theano.py ├── rntn_tensorflow.py ├── rntn_tensorflow_rnn.py ├── rntn_theano.py ├── tfidf_tsne.py ├── util.py ├── visualize_countries.py ├── w2v_model.npz ├── w2v_word2idx.json ├── word2vec.py ├── word2vec_tf.py └── word2vec_theano.py ├── nlp_class3 ├── attention.py ├── bilstm_mnist.py ├── bilstm_test.py ├── cnn_toxic.py ├── convert_twitter.py ├── extra_reading.txt ├── lstm_toxic.py ├── memory_network.py ├── poetry.py ├── simple_rnn_test.py └── wseq2seq.py ├── nlp_v2 ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── numpy_class ├── classification_example.py ├── dot_for.py ├── exercises │ ├── ex1.py │ ├── ex2.py │ ├── ex3.py │ ├── ex4.py │ ├── ex5.py │ ├── ex6.py │ ├── ex7.py │ ├── ex8.py │ └── ex9.py ├── manual_data_loading.py ├── python3 │ ├── dot_for.py │ └── manual_data_loading.py ├── regression_example.py ├── table1.csv └── table2.csv ├── prophet └── extra_reading.txt ├── pytorch ├── .gitignore ├── WHERE ARE THE NOTEBOOKS.txt ├── aapl_msi_sbux.csv ├── ann_regression.py ├── exercises.txt ├── extra_reading.txt ├── plot_rl_rewards.py └── rl_trader.py ├── recommenders ├── autorec.py ├── extra_reading.txt ├── itembased.py ├── mf.py ├── mf2.py ├── mf_keras.py ├── mf_keras_deep.py ├── mf_keras_res.py ├── preprocess.py ├── preprocess2dict.py ├── preprocess2sparse.py ├── preprocess_shrink.py ├── rbm_tf_k.py ├── rbm_tf_k_faster.py ├── spark.py ├── spark2.py ├── tfidf.py └── userbased.py ├── rl ├── approx_control.py ├── approx_prediction.py ├── bayesian_bandit.py ├── bayesian_normal.py ├── bayesian_starter.py ├── cartpole.py ├── cartpole_gym0.19.py ├── comparing_epsilons.py ├── comparing_explore_exploit_methods.py ├── epsilon_greedy.py ├── epsilon_greedy_starter.py ├── extra_reading.txt ├── grid_world.py ├── iterative_policy_evaluation_deterministic.py ├── iterative_policy_evaluation_probabilistic.py ├── linear_rl_trader.py ├── monte_carlo.py ├── monte_carlo_es.py ├── monte_carlo_no_es.py ├── optimistic.py ├── optimistic_initial_values.py ├── optimistic_starter.py ├── plot_rl_rewards.py ├── policy_iteration_deterministic.py ├── policy_iteration_probabilistic.py ├── q_learning.py ├── sarsa.py ├── td0_prediction.py ├── tic_tac_toe.py ├── ucb1.py ├── ucb1_starter.py └── value_iteration.py ├── rl2 ├── a3c │ ├── main.py │ ├── nets.py │ ├── thread_example.py │ └── worker.py ├── atari │ ├── dqn_tf.py │ └── dqn_theano.py ├── cartpole │ ├── dqn_tf.py │ ├── dqn_theano.py │ ├── pg_tf.py │ ├── pg_theano.py │ ├── q_learning.py │ ├── q_learning_bins.py │ ├── random_search.py │ ├── save_a_video.py │ ├── td_lambda.py │ ├── tf_warmup.py │ └── theano_warmup.py ├── extra_reading.txt ├── gym_tutorial.py └── mountaincar │ ├── n_step.py │ ├── pg_tf.py │ ├── pg_tf_random.py │ ├── pg_theano.py │ ├── pg_theano_random.py │ ├── q_learning.py │ └── td_lambda.py ├── rl3 ├── a2c │ ├── a2c.py │ ├── atari_wrappers.py │ ├── main.py │ ├── neural_network.py │ ├── play.py │ └── subproc_vec_env.py ├── ddpg.py ├── es_flappy.py ├── es_mnist.py ├── es_mujoco.py ├── es_simple.py ├── extra_reading.txt ├── flappy2envs.py ├── gym_review.py ├── plot_ddpg_result.py ├── plot_es_flappy_results.py ├── plot_es_mujoco_results.py └── sample_test.py ├── rnn_class ├── WHERE ARE THE NOTEBOOKS.txt ├── __init__.py ├── batch_gru.py ├── batch_parity.py ├── batch_units.py ├── batch_wiki.py ├── brown.py ├── exercises.txt ├── extra_reading.txt ├── gru.py ├── gru_nonorm_part1_wikipedia_word2idx.json ├── gru_nonorm_part1_word_embeddings.npy ├── lstm.py ├── mlp_parity.py ├── poetry_classifier.py ├── rrnn_language.py ├── srn_language.py ├── srn_language_tf.py ├── srn_parity.py ├── srn_parity_tf.py ├── tf_parity.py ├── util.py ├── visualize_embeddings.py └── wiki.py ├── stats └── extra_reading.txt ├── supervised_class ├── app.py ├── app_caller.py ├── app_trainer.py ├── bayes.py ├── dt.py ├── dt_without_recursion.py ├── knn.py ├── knn_donut.py ├── knn_fail.py ├── knn_vectorized.py ├── knn_xor.py ├── multinomialnb.py ├── nb.py ├── perceptron.py ├── regression.py └── util.py ├── supervised_class2 ├── adaboost.py ├── bagging_classification.py ├── bagging_regression.py ├── bias_variance_demo.py ├── bootstrap.py ├── extra_reading.txt ├── knn_dt_demo.py ├── rf_classification.py ├── rf_regression.py ├── rf_vs_bag.py ├── rf_vs_bag2.py └── util.py ├── svm_class ├── crossval.py ├── extra_reading.txt ├── fake_neural_net.py ├── kernel_svm_gradient_primal.py ├── linear_svm_gradient.py ├── rbfnetwork.py ├── real_neural_net.py ├── regression.py ├── svm_gradient.py ├── svm_medical.py ├── svm_mnist.py ├── svm_smo.py ├── svm_spam.py └── util.py ├── tensorflow ├── MNIST_data │ ├── t10k-images-idx3-ubyte.gz │ ├── t10k-labels-idx1-ubyte.gz │ ├── train-images-idx3-ubyte.gz │ └── train-labels-idx1-ubyte.gz └── input_data.py ├── tf2.0 ├── .gitignore ├── WHERE ARE THE NOTEBOOKS.txt ├── aapl_msi_sbux.csv ├── auto-mpg.data ├── daily-minimum-temperatures-in-me.csv ├── exercises.txt ├── extra_reading.txt ├── fake_util.py ├── moore.csv ├── plot_rl_rewards.py ├── rl_trader.py ├── sbux.csv └── xor3d.py ├── timeseries ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── transformers ├── WHERE ARE THE NOTEBOOKS.txt └── extra_reading.txt ├── unsupervised_class ├── __init__.py ├── books.py ├── choose_k.py ├── evolution.py ├── gmm.py ├── gmm_mnist.py ├── hcluster.py ├── kmeans.py ├── kmeans_fail.py ├── kmeans_mnist.py ├── kmeans_visualize.py ├── neural_kmeans.py └── tweets.py ├── unsupervised_class2 ├── __init__.py ├── autoencoder.py ├── autoencoder_tf.py ├── compare_pca_svd.py ├── extra_reading.txt ├── gaussian_nb.py ├── pca.py ├── pca_impl.py ├── rbm.py ├── rbm_tf.py ├── sk_mlp.py ├── tsne_books.py ├── tsne_donut.py ├── tsne_mnist.py ├── tsne_visualization.py ├── tsne_xor.py ├── umap_transformer.py ├── unsupervised.py ├── util.py ├── vanishing.py ├── visualize_features.py └── xwing.py └── unsupervised_class3 ├── autoencoder_tf.py ├── autoencoder_theano.py ├── bayes_classifier_gaussian.py ├── bayes_classifier_gmm.py ├── dcgan_tf.py ├── dcgan_theano.py ├── extra_reading.txt ├── parameterize_guassian.py ├── test_stochastic_tensor.py ├── util.py ├── vae_tf.py ├── vae_theano.py └── visualize_latent_space.py /.gitignore: -------------------------------------------------------------------------------- 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