Cluster Analysis and Unsupervised Machine Learning in Python | B1E34E8204AC2E9508988B4FA204387C80D37707

Cluster Analysis and Unsupervised Machine Learning in Python

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/z.9781836649373_Code/ann_class2/
__init__.py	0.0 KB
/.../ann_logistic_extra/
__init__.py	0.0 KB
ann_predict.py	0.9 KB
ann_train.py	2.2 KB
ecommerce_data.csv	12.4 KB
logistic_predict.py	0.7 KB
logistic_softmax_train.py	1.8 KB
logistic_train.py	1.5 KB
process.py	1.8 KB
/z.9781836649373_Code/hmm_class/
__init__.py	0.0 KB
coin_data.txt	1.6 KB
edgar_allan_poe.txt	26.6 KB
extra_reading.txt	0.4 KB
frost.py	3.1 KB
generate_c.py	2.3 KB
generate_ht.py	1.3 KB
helloworld.wav	36.9 KB
hmm_classifier.py	3.1 KB
hmmc.py	10.0 KB
hmmc_concat.py	8.8 KB
hmmc_scaled_concat.py	9.0 KB
hmmc_scaled_concat_diag.py	10.3 KB
hmmc_tf.py	7.8 KB
hmmc_theano.py	6.5 KB
hmmc_theano2.py	7.0 KB
hmmd.py	7.3 KB
hmmd_scaled.py	6.3 KB
hmmd_tf.py	4.8 KB
hmmd_theano.py	4.6 KB
hmmd_theano2.py	4.9 KB
robert_frost.txt	56.3 KB
scan1.py	0.7 KB
scan2.py	0.8 KB
scan3.py	1.0 KB
site_data.csv	419.9 KB
sites.py	1.0 KB
tf_scan1.py	0.9 KB
tf_scan2.py	0.9 KB
tf_scan3.py	1.2 KB
/z.9781836649373_Code/rnn_class/
__init__.py	0.0 KB
batch_gru.py	2.4 KB
batch_parity.py	5.9 KB
batch_units.py	6.8 KB
batch_wiki.py	7.0 KB
brown.py	2.9 KB
exercises.txt	0.6 KB
extra_reading.txt	0.1 KB
gru.py	2.0 KB
gru_nonorm_part1_wikipedia_word2idx.json	31.1 KB
gru_nonorm_part1_word_embeddings.npy	1.3 MB
lstm.py	2.9 KB
mlp_parity.py	4.1 KB
poetry_classifier.py	4.9 KB
rrnn_language.py	6.9 KB
srn_language.py	7.7 KB
srn_language_tf.py	7.8 KB
srn_parity.py	3.5 KB
srn_parity_tf.py	3.4 KB
tf_parity.py	4.5 KB
util.py	9.0 KB
visualize_embeddings.py	1.3 KB
WHERE ARE THE NOTEBOOKS.txt	0.3 KB
wiki.py	6.7 KB
/z.9781836649373_Code/unsupervised_class/
__init__.py	0.0 KB
books.py	8.0 KB
choose_k.py	0.8 KB
evolution.py	2.7 KB
gmm.py	3.2 KB
gmm_mnist.py	1.2 KB
hcluster.py	1.8 KB
kmeans.py	3.5 KB
kmeans_fail.py	1.6 KB
kmeans_mnist.py	4.5 KB
kmeans_visualize.py	2.5 KB
neural_kmeans.py	0.9 KB
tweets.py	5.2 KB
/z.9781836649373_Code/unsupervised_class2/
__init__.py	0.0 KB
autoencoder.py	8.6 KB
autoencoder_tf.py	7.9 KB
compare_pca_svd.py	0.8 KB
extra_reading.txt	1.3 KB
gaussian_nb.py	1.8 KB
pca.py	1.0 KB
pca_impl.py	1.1 KB
rbm.py	4.5 KB
rbm_tf.py	4.9 KB
sk_mlp.py	0.8 KB
tsne_books.py	3.0 KB
tsne_donut.py	1.3 KB
tsne_mnist.py	1.3 KB
tsne_visualization.py	1.4 KB
tsne_xor.py	1.0 KB
umap_transformer.py	1.2 KB
unsupervised.py	4.9 KB
util.py	1.0 KB
vanishing.py	3.9 KB
visualize_features.py	1.9 KB
xwing.py	3.9 KB
Chapter 7 Setting Up Your Environment (Appendix)/
002. Anaconda Environment Setup.mp4	69.7 MB
001. Pre-Installation Check.en.srt	7.1 KB
001. Pre-Installation Check.mp4	11.6 MB
002. Anaconda Environment Setup.en.srt	22.3 KB
003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.en.srt	16.3 KB
003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.mp4	51.5 MB
Chapter 2 Getting Set Up/
001. Where to get the code.mp4	10.4 MB
001. Where to get the code.en.srt	6.5 KB
Chapter 3 Unsupervised Learning/
001. What is unsupervised learning used for.en.srt	7.9 KB
001. What is unsupervised learning used for.mp4	13.1 MB
002. Why Use Clustering.en.srt	13.2 KB
002. Why Use Clustering.mp4	22.3 MB
Chapter 4 K-Means Clustering/
001. An Easy Introduction to K-Means Clustering.en.srt	10.3 KB
001. An Easy Introduction to K-Means Clustering.mp4	18.4 MB
002. Hard K-Means Exercise Prompt 1.en.srt	12.7 KB
002. Hard K-Means Exercise Prompt 1.mp4	25.9 MB
003. Hard K-Means Exercise 1 Solution.en.srt	15.2 KB
003. Hard K-Means Exercise 1 Solution.mp4	30.6 MB
004. Hard K-Means Exercise Prompt 2.en.srt	6.6 KB
004. Hard K-Means Exercise Prompt 2.mp4	12.2 MB
005. Hard K-Means Exercise 2 Solution.en.srt	9.2 KB
005. Hard K-Means Exercise 2 Solution.mp4	18.1 MB
006. Hard K-Means Exercise Prompt 3.en.srt	9.7 KB
006. Hard K-Means Exercise Prompt 3.mp4	20.0 MB
007. Hard K-Means Exercise 3 Solution.en.srt	22.6 KB
007. Hard K-Means Exercise 3 Solution.mp4	47.8 MB
008. Hard K-Means Objective Theory.en.srt	18.4 KB
008. Hard K-Means Objective Theory.mp4	29.9 MB
009. Hard K-Means Objective Code.en.srt	6.6 KB
009. Hard K-Means Objective Code.mp4	14.5 MB
010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).en.srt	4.1 KB
010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).mp4	4.3 MB
011. Soft K-Means.en.srt	7.8 KB
011. Soft K-Means.mp4	10.6 MB
012. The K-Means Objective Function.en.srt	2.3 KB
012. The K-Means Objective Function.mp4	3.2 MB
013. Soft K-Means in Python Code.en.srt	9.7 KB
013. Soft K-Means in Python Code.mp4	30.4 MB
014. How to Pace Yourself.en.srt	5.0 KB
014. How to Pace Yourself.mp4	8.1 MB
015. Visualizing Each Step of K-Means.en.srt	3.0 KB
015. Visualizing Each Step of K-Means.mp4	8.2 MB
016. Examples of where K-Means can fail.en.srt	6.8 KB
016. Examples of where K-Means can fail.mp4	21.1 MB
017. Disadvantages of K-Means Clustering.en.srt	3.5 KB
017. Disadvantages of K-Means Clustering.mp4	3.9 MB
018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).en.srt	9.6 KB
018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4	13.4 MB
019. Using K-Means on Real Data MNIST.en.srt	7.4 KB
019. Using K-Means on Real Data MNIST.mp4	18.2 MB
020. One Way to Choose K.en.srt	5.8 KB
020. One Way to Choose K.mp4	10.9 MB
021. K-Means Application Finding Clusters of Related Words.en.srt	9.4 KB
021. K-Means Application Finding Clusters of Related Words.mp4	37.2 MB
022. Clustering for NLP and Computer Vision Real-World Applications.en.srt	9.7 KB
022. Clustering for NLP and Computer Vision Real-World Applications.mp4	20.4 MB
023. Suggestion Box.en.srt	4.7 KB
023. Suggestion Box.mp4	11.7 MB
Chapter 5 Hierarchical Clustering/
001. Visual Walkthrough of Agglomerative Hierarchical Clustering.en.srt	4.0 KB
001. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4	4.0 MB
002. Agglomerative Clustering Options.en.srt	5.7 KB
002. Agglomerative Clustering Options.mp4	5.8 MB
003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.en.srt	5.1 KB
003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4	11.5 MB
004. Application Evolution.en.srt	18.5 KB
004. Application Evolution.mp4	39.5 MB
005. Application Donald Trump vs. Hillary Clinton Tweets.en.srt	21.0 KB
005. Application Donald Trump vs. Hillary Clinton Tweets.mp4	52.8 MB
Chapter 6 Gaussian Mixture Models (GMMs)/
001. Gaussian Mixture Model (GMM) Algorithm.en.srt	22.2 KB
001. Gaussian Mixture Model (GMM) Algorithm.mp4	31.9 MB
002. Write a Gaussian Mixture Model in Python Code.en.srt	27.0 KB
002. Write a Gaussian Mixture Model in Python Code.mp4	65.8 MB
003. Practical Issues with GMM.en.srt	13.9 KB
003. Practical Issues with GMM.mp4	20.2 MB
004. Comparison between GMM and K-Means.en.srt	5.5 KB
004. Comparison between GMM and K-Means.mp4	9.4 MB
005. Kernel Density Estimation.en.srt	9.3 KB
005. Kernel Density Estimation.mp4	14.3 MB
006. GMM vs Bayes Classifier (pt 1).en.srt	13.6 KB
006. GMM vs Bayes Classifier (pt 1).mp4	20.5 MB
007. GMM vs Bayes Classifier (pt 2).en.srt	16.3 KB
007. GMM vs Bayes Classifier (pt 2).mp4	23.2 MB
008. Expectation-Maximization (pt 1).en.srt	16.3 KB
008. Expectation-Maximization (pt 1).mp4	24.5 MB
009. Expectation-Maximization (pt 2).en.srt	3.0 KB
009. Expectation-Maximization (pt 2).mp4	5.2 MB
010. Expectation-Maximization (pt 3).en.srt	11.1 KB
010. Expectation-Maximization (pt 3).mp4	16.2 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/
001. How to Code Yourself (part 1).en.srt	23.5 KB
001. How to Code Yourself (part 1).mp4	31.0 MB
002. How to Code Yourself (part 2).en.srt	14.2 KB
002. How to Code Yourself (part 2).mp4	20.0 MB
003. Proof that using Jupyter Notebook is the same as not using it.en.srt	16.3 KB
003. Proof that using Jupyter Notebook is the same as not using it.mp4	36.2 MB
004. How to use Github & Extra Coding Tips (Optional).en.srt	17.0 KB
004. How to use Github & Extra Coding Tips (Optional).mp4	30.6 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/
001. How to Succeed in this Course (Long Version).en.srt	16.4 KB
001. How to Succeed in this Course (Long Version).mp4	18.1 MB
002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.en.srt	34.6 KB
002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	43.6 MB
003. What order should I take your courses in (part 1).en.srt	18.4 KB
003. What order should I take your courses in (part 1).mp4	29.5 MB
004. What order should I take your courses in (part 2).en.srt	26.0 KB
004. What order should I take your courses in (part 2).mp4	39.3 MB
z.9781836649373_Code/
.gitignore	0.1 KB
best_fit_line.py	1.1 KB
README.md	6.9 KB
/z.9781836649373_Code/ab_testing/
advertisement_clicks.csv	8.0 KB
bayesian_bandit.py	1.9 KB
bayesian_normal.py	2.1 KB
bayesian_starter.py	1.9 KB
cdfs_and_percentiles.py	0.6 KB
chisquare.py	2.0 KB
ci_comparison.py	1.2 KB
client.py	1.3 KB
comparing_epsilons.py	2.2 KB
convergence.py	1.1 KB
demo.py	1.0 KB
epsilon_greedy.py	2.5 KB
epsilon_greedy_starter.py	2.2 KB
ex_chisq.py	1.2 KB
ex_ttest.py	1.2 KB
extra_reading.txt	0.8 KB
optimistic.py	1.9 KB
optimistic_starter.py	1.8 KB
server_solution.py	1.7 KB
server_starter.py	1.3 KB
ttest.py	1.0 KB
ucb1.py	2.2 KB
ucb1_starter.py	2.1 KB
/z.9781836649373_Code/airline/
ann.py	5.6 KB
international-airline-passengers.csv	2.3 KB
lr.py	1.8 KB
rnn.py	5.1 KB
/z.9781836649373_Code/ann_class/
backprop.py	4.2 KB
extra_reading.txt	0.5 KB
forwardprop.py	1.8 KB
regression.py	3.0 KB
sklearn_ann.py	1.0 KB
tf_example.py	2.5 KB
xor_donut.py	4.5 KB
Chapter 1 Welcome/
001. Introduction.en.srt	7.6 KB
001. Introduction.mp4	14.3 MB
002. Course Outline.en.srt	6.7 KB
002. Course Outline.mp4	9.9 MB
003. Special Offer.en.srt	1.7 KB
003. Special Offer.mp4	3.3 MB
/z.9781836649373_Code/ann_class2/
adam.py	6.1 KB
batch_norm_tf.py	5.7 KB
batch_norm_theano.py	5.9 KB
cntk_example.py	3.3 KB
dropout_tensorflow.py	5.0 KB
dropout_theano.py	5.1 KB
extra_reading.txt	1.2 KB
grid_search.py	2.6 KB
keras_example.py	2.3 KB
keras_functional.py	2.2 KB
mlp.py	1.2 KB
momentum.py	6.3 KB
mxnet_example.py	2.6 KB
pytorch_batchnorm.py	5.0 KB
pytorch_dropout.py	5.1 KB
pytorch_example.py	3.7 KB
pytorch_example2.py	4.6 KB
random_search.py	2.3 KB
rmsprop.py	4.7 KB
rmsprop_test.py	3.0 KB
sgd.py	5.4 KB
tensorflow1.py	3.1 KB
tensorflow2.py	3.8 KB
tf_with_save.py	4.3 KB
theano_ann.py	3.9 KB
theano1.py	1.8 KB
theano2.py	3.5 KB
util.py	8.1 KB
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nb.py	4.4 KB
Q.csv	122.0 KB
README	0.8 KB
Xtest.csv	241.0 KB
Xtrain.csv	1.4 MB
ytest.csv	4.0 KB
ytrain.csv	23.6 KB
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em.py	1.6 KB
probit.py	3.7 KB
Q.csv	122.0 KB
README	0.8 KB
Xtest.csv	241.0 KB
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ytest.csv	4.0 KB
ytrain.csv	23.6 KB
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run.py	4.0 KB
X_set1.csv	97.3 KB
X_set2.csv	610.1 KB
X_set3.csv	2.4 MB
y_set1.csv	0.8 KB
y_set2.csv	2.0 KB
y_set3.csv	3.9 KB
z_set1.csv	0.8 KB
z_set2.csv	1.9 KB
z_set3.csv	3.8 KB
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data.txt	3.8 KB
emgmm.py	1.9 KB
npbgmm.py	6.5 KB
vigmm.py	6.4 KB
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extra_reading.txt	0.1 KB
WHERE ARE THE NOTEBOOKS.txt	0.3 KB
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extra_reading.txt	0.1 KB
WHERE ARE THE NOTEBOOKS.txt	0.3 KB
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exercises.txt	0.8 KB
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helloworld.wav	36.9 KB
keras_example.py	2.9 KB
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class_activation_maps.py	2.4 KB
extra_reading.txt	0.5 KB
fashion.py	2.7 KB
fashion2.py	2.5 KB
make_limited_datasets.py	0.9 KB
siamese.py	11.8 KB
ssd.py	4.7 KB
style_transfer1.py	4.8 KB
style_transfer2.py	4.1 KB
style_transfer3.py	3.9 KB
test_softmax.py	0.8 KB
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tf_resnet_convblock.py	6.2 KB
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sydney.jpg	81.2 KB
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flowercarrier.jpg	95.5 KB
lesdemoisellesdavignon.jpg	181.3 KB
monalisa.jpg	231.4 KB
starrynight.jpg	34.3 KB
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legend.txt	6.7 KB
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X.txt	20.1 KB
X_orig.txt	9.2 KB
y.txt	1.4 KB
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go_here_instead.txt	0.1 KB
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ann.py	1.8 KB
basic_mlp.py	1.1 KB
batchnorm.py	1.9 KB
cnn.py	2.0 KB
cnn_cifar.py	2.3 KB
cnn_dropout_batchnorm.py	2.1 KB
dropout.py	1.9 KB
sentiment_analysis.py	2.7 KB
sine.py	1.4 KB
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translation.py	4.1 KB
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makelist.py	0.2 KB
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WHERE ARE THE NOTEBOOKS.txt	0.3 KB
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data_1d.csv	2.8 KB
data_2d.csv	4.2 KB
data_poly.csv	2.8 KB
gd.py	0.3 KB
generate_1d.py	0.6 KB
generate_2d.py	0.7 KB
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gradient_descent.py	1.2 KB
l1_regularization.py	1.3 KB
l2_regularization.py	1.2 KB
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lr_2d.py	1.5 KB
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mlr02.xls	0.8 KB
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logistic2.py	1.4 KB
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positive.review	1.1 MB
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glove_svd.py	6.8 KB
glove_tf.py	8.2 KB
glove_theano.py	8.6 KB
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markov.py	4.2 KB
ner.txt	356.6 KB
ner_baseline.py	3.7 KB
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ner_tf.py	5.9 KB
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neural_network2.py	4.9 KB
pmi.py	9.2 KB
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tfidf_tsne.py	3.1 KB
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visualize_countries.py	1.1 KB
w2v_model.npz	160.5 KB
w2v_word2idx.json	31.5 KB
word2vec.py	10.6 KB
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poetry.py	6.1 KB
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wseq2seq.py	10.7 KB
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ex3.py	0.8 KB
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fight.mp4	616.7 KB
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robots_playing_soccer.jpeg	140.3 KB
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exercises.txt	1.1 KB
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plot_rl_rewards.py	0.5 KB
rl_trader.py	11.9 KB
WHERE ARE THE NOTEBOOKS.txt	0.3 KB
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autorec.py	3.1 KB
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itembased.py	5.1 KB
mf.py	3.7 KB
mf_keras.py	2.7 KB
mf_keras_deep.py	2.3 KB
mf_keras_res.py	2.4 KB
mf2.py	4.3 KB
preprocess.py	1.1 KB
preprocess_shrink.py	1.9 KB
preprocess2dict.py	2.3 KB
preprocess2sparse.py	1.7 KB
rbm_tf_k.py	8.5 KB
rbm_tf_k_faster.py	7.4 KB
spark.py	1.6 KB
spark2.py	1.8 KB
tfidf.py	2.1 KB
userbased.py	5.1 KB
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approx_control.py	4.3 KB
approx_prediction.py	3.4 KB
bayesian_bandit.py	1.9 KB
bayesian_normal.py	2.1 KB
bayesian_starter.py	1.9 KB
cartpole.py	4.0 KB
cartpole_gym0.19.py	3.7 KB
comparing_epsilons.py	1.8 KB
comparing_explore_exploit_methods.py	3.0 KB
epsilon_greedy.py	2.3 KB
epsilon_greedy_starter.py	2.2 KB
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grid_world.py	9.8 KB
iterative_policy_evaluation_deterministic.py	3.1 KB
iterative_policy_evaluation_probabilistic.py	3.2 KB
linear_rl_trader.py	9.8 KB
monte_carlo.py	2.8 KB
monte_carlo_es.py	4.2 KB
monte_carlo_no_es.py	4.4 KB
optimistic.py	1.9 KB
optimistic_initial_values.py	1.8 KB
optimistic_starter.py	1.8 KB
plot_rl_rewards.py	0.6 KB
policy_iteration_deterministic.py	4.2 KB
policy_iteration_probabilistic.py	4.1 KB
q_learning.py	2.6 KB
sarsa.py	2.6 KB
td0_prediction.py	2.1 KB
tic_tac_toe.py	13.0 KB
ucb1.py	2.2 KB
ucb1_starter.py	2.1 KB
value_iteration.py	3.1 KB
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gym_tutorial.py	1.5 KB
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thread_example.py	1.0 KB
worker.py	9.0 KB
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dqn_tf.py	13.6 KB
dqn_theano.py	14.4 KB
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pg_tf.py	7.9 KB
pg_theano.py	7.3 KB
q_learning.py	4.7 KB
q_learning_bins.py	4.1 KB
random_search.py	1.7 KB
save_a_video.py	1.8 KB
td_lambda.py	4.4 KB
tf_warmup.py	1.5 KB
theano_warmup.py	1.0 KB
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n_step.py	4.8 KB
pg_tf.py	6.7 KB
pg_tf_random.py	7.3 KB
pg_theano.py	7.2 KB
pg_theano_random.py	6.2 KB
q_learning.py	6.6 KB
td_lambda.py	3.9 KB
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ddpg.py	11.0 KB
es_flappy.py	6.1 KB
es_mnist.py	3.7 KB
es_mujoco.py	4.8 KB
es_simple.py	1.3 KB
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flappy2envs.py	4.3 KB
gym_review.py	1.5 KB
plot_ddpg_result.py	0.7 KB
plot_es_flappy_results.py	0.5 KB
plot_es_mujoco_results.py	0.5 KB
sample_test.py	0.4 KB
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a2c.py	8.7 KB
atari_wrappers.py	10.1 KB
main.py	1.7 KB
neural_network.py	1.8 KB
play.py	1.8 KB
subproc_vec_env.py	3.4 KB
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app.py	1.4 KB
app_caller.py	1.0 KB
app_trainer.py	0.8 KB
bayes.py	2.3 KB
dt.py	6.9 KB
dt_without_recursion.py	10.6 KB
knn.py	3.2 KB
knn_donut.py	0.7 KB
knn_fail.py	1.1 KB
knn_vectorized.py	3.1 KB
knn_xor.py	0.7 KB
multinomialnb.py	2.0 KB
nb.py	2.1 KB
perceptron.py	3.1 KB
regression.py	0.9 KB
util.py	1.7 KB
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adaboost.py	2.6 KB
bagging_classification.py	2.2 KB
bagging_regression.py	1.9 KB
bias_variance_demo.py	4.0 KB
bootstrap.py	1.4 KB
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knn_dt_demo.py	4.3 KB
rf_classification.py	3.6 KB
rf_regression.py	4.3 KB
rf_vs_bag.py	1.8 KB
rf_vs_bag2.py	2.9 KB
util.py	2.5 KB
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crossval.py	0.9 KB
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fake_neural_net.py	4.2 KB
kernel_svm_gradient_primal.py	6.0 KB
linear_svm_gradient.py	4.5 KB
rbfnetwork.py	2.4 KB
real_neural_net.py	1.2 KB
regression.py	1.4 KB
svm_gradient.py	4.8 KB
svm_medical.py	1.0 KB
svm_mnist.py	0.8 KB
svm_smo.py	9.5 KB
svm_spam.py	2.5 KB
util.py	4.5 KB
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input_data.py	5.9 KB
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t10k-images-idx3-ubyte.gz	1.6 MB
t10k-labels-idx1-ubyte.gz	4.5 KB
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train-labels-idx1-ubyte.gz	28.9 KB
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aapl_msi_sbux.csv	24.1 KB
auto-mpg.data	30.3 KB
daily-minimum-temperatures-in-me.csv	68.1 KB
exercises.txt	1.1 KB
extra_reading.txt	1.4 KB
fake_util.py	0.1 KB
moore.csv	2.3 KB
plot_rl_rewards.py	0.5 KB
rl_trader.py	11.0 KB
sbux.csv	61.9 KB
WHERE ARE THE NOTEBOOKS.txt	0.3 KB
xor3d.py	0.6 KB
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WHERE ARE THE NOTEBOOKS.txt	0.3 KB
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WHERE ARE THE NOTEBOOKS.txt	0.3 KB
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autoencoder_tf.py	2.7 KB
autoencoder_theano.py	2.9 KB
bayes_classifier_gaussian.py	1.8 KB
bayes_classifier_gmm.py	2.2 KB
dcgan_tf.py	16.4 KB
dcgan_theano.py	18.5 KB
extra_reading.txt	0.5 KB
parameterize_guassian.py	1.3 KB
test_stochastic_tensor.py	1.1 KB
util.py	5.0 KB
vae_tf.py	8.9 KB
vae_theano.py	7.7 KB
visualize_latent_space.py	1.9 KB
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