TorBT - Torrents and Magnet Links Search Engine

Cluster Analysis and Unsupervised Machine Learning in Python

File Name
Size
z.9781836649373_Code/ann_class2/__init__.py
0 B
z.9781836649373_Code/ann_logistic_extra/__init__.py
0 B
z.9781836649373_Code/hmm_class/__init__.py
0 B
z.9781836649373_Code/rnn_class/__init__.py
0 B
z.9781836649373_Code/unsupervised_class/__init__.py
0 B
z.9781836649373_Code/unsupervised_class2/__init__.py
0 B
Chapter 7 Setting Up Your Environment (Appendix)/002. Anaconda Environment Setup.mp4
66 MB
Chapter 2 Getting Set Up/001. Where to get the code.mp4
9.9 MB
Chapter 3 Unsupervised Learning/001. What is unsupervised learning used for.en.srt
7.7 kB
Chapter 3 Unsupervised Learning/001. What is unsupervised learning used for.mp4
12 MB
Chapter 3 Unsupervised Learning/002. Why Use Clustering.en.srt
13 kB
Chapter 3 Unsupervised Learning/002. Why Use Clustering.mp4
21 MB
Chapter 4 K-Means Clustering/001. An Easy Introduction to K-Means Clustering.en.srt
10 kB
Chapter 4 K-Means Clustering/001. An Easy Introduction to K-Means Clustering.mp4
18 MB
Chapter 4 K-Means Clustering/002. Hard K-Means Exercise Prompt 1.en.srt
12 kB
Chapter 4 K-Means Clustering/002. Hard K-Means Exercise Prompt 1.mp4
25 MB
Chapter 4 K-Means Clustering/003. Hard K-Means Exercise 1 Solution.en.srt
15 kB
Chapter 4 K-Means Clustering/003. Hard K-Means Exercise 1 Solution.mp4
29 MB
Chapter 4 K-Means Clustering/004. Hard K-Means Exercise Prompt 2.en.srt
6.5 kB
Chapter 4 K-Means Clustering/004. Hard K-Means Exercise Prompt 2.mp4
12 MB
Chapter 4 K-Means Clustering/005. Hard K-Means Exercise 2 Solution.en.srt
9.0 kB
Chapter 4 K-Means Clustering/005. Hard K-Means Exercise 2 Solution.mp4
17 MB
Chapter 4 K-Means Clustering/006. Hard K-Means Exercise Prompt 3.en.srt
9.5 kB
Chapter 4 K-Means Clustering/006. Hard K-Means Exercise Prompt 3.mp4
19 MB
Chapter 4 K-Means Clustering/007. Hard K-Means Exercise 3 Solution.en.srt
22 kB
Chapter 4 K-Means Clustering/007. Hard K-Means Exercise 3 Solution.mp4
46 MB
Chapter 4 K-Means Clustering/008. Hard K-Means Objective Theory.en.srt
18 kB
Chapter 4 K-Means Clustering/008. Hard K-Means Objective Theory.mp4
28 MB
Chapter 4 K-Means Clustering/009. Hard K-Means Objective Code.en.srt
6.5 kB
Chapter 4 K-Means Clustering/009. Hard K-Means Objective Code.mp4
14 MB
Chapter 4 K-Means Clustering/010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).en.srt
4.0 kB
Chapter 4 K-Means Clustering/010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).mp4
4.1 MB
Chapter 4 K-Means Clustering/011. Soft K-Means.en.srt
7.6 kB
Chapter 4 K-Means Clustering/011. Soft K-Means.mp4
10 MB
Chapter 4 K-Means Clustering/012. The K-Means Objective Function.en.srt
2.3 kB
Chapter 4 K-Means Clustering/012. The K-Means Objective Function.mp4
3.1 MB
Chapter 4 K-Means Clustering/013. Soft K-Means in Python Code.en.srt
9.5 kB
Chapter 4 K-Means Clustering/013. Soft K-Means in Python Code.mp4
29 MB
Chapter 4 K-Means Clustering/014. How to Pace Yourself.en.srt
4.9 kB
Chapter 4 K-Means Clustering/014. How to Pace Yourself.mp4
7.8 MB
Chapter 4 K-Means Clustering/015. Visualizing Each Step of K-Means.en.srt
2.9 kB
Chapter 4 K-Means Clustering/015. Visualizing Each Step of K-Means.mp4
7.8 MB
Chapter 4 K-Means Clustering/016. Examples of where K-Means can fail.en.srt
6.7 kB
Chapter 4 K-Means Clustering/016. Examples of where K-Means can fail.mp4
20 MB
Chapter 4 K-Means Clustering/017. Disadvantages of K-Means Clustering.en.srt
3.4 kB
Chapter 4 K-Means Clustering/017. Disadvantages of K-Means Clustering.mp4
3.7 MB
Chapter 4 K-Means Clustering/018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).en.srt
9.4 kB
Chapter 4 K-Means Clustering/018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4
13 MB
Chapter 4 K-Means Clustering/019. Using K-Means on Real Data MNIST.en.srt
7.2 kB
Chapter 4 K-Means Clustering/019. Using K-Means on Real Data MNIST.mp4
17 MB
Chapter 4 K-Means Clustering/020. One Way to Choose K.en.srt
5.7 kB
Chapter 4 K-Means Clustering/020. One Way to Choose K.mp4
10 MB
Chapter 4 K-Means Clustering/021. K-Means Application Finding Clusters of Related Words.en.srt
9.1 kB
Chapter 4 K-Means Clustering/021. K-Means Application Finding Clusters of Related Words.mp4
35 MB
Chapter 4 K-Means Clustering/022. Clustering for NLP and Computer Vision Real-World Applications.en.srt
9.4 kB
Chapter 4 K-Means Clustering/022. Clustering for NLP and Computer Vision Real-World Applications.mp4
20 MB
Chapter 4 K-Means Clustering/023. Suggestion Box.en.srt
4.6 kB
Chapter 4 K-Means Clustering/023. Suggestion Box.mp4
11 MB
Chapter 5 Hierarchical Clustering/001. Visual Walkthrough of Agglomerative Hierarchical Clustering.en.srt
3.9 kB
Chapter 5 Hierarchical Clustering/001. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4
3.8 MB
Chapter 5 Hierarchical Clustering/002. Agglomerative Clustering Options.en.srt
5.6 kB
Chapter 5 Hierarchical Clustering/002. Agglomerative Clustering Options.mp4
5.5 MB
Chapter 5 Hierarchical Clustering/003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.en.srt
5.0 kB
Chapter 5 Hierarchical Clustering/003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4
11 MB
Chapter 5 Hierarchical Clustering/004. Application Evolution.en.srt
18 kB
Chapter 5 Hierarchical Clustering/004. Application Evolution.mp4
38 MB
Chapter 5 Hierarchical Clustering/005. Application Donald Trump vs. Hillary Clinton Tweets.en.srt
20 kB
Chapter 5 Hierarchical Clustering/005. Application Donald Trump vs. Hillary Clinton Tweets.mp4
50 MB
Chapter 6 Gaussian Mixture Models (GMMs)/001. Gaussian Mixture Model (GMM) Algorithm.en.srt
22 kB
Chapter 6 Gaussian Mixture Models (GMMs)/001. Gaussian Mixture Model (GMM) Algorithm.mp4
30 MB
Chapter 6 Gaussian Mixture Models (GMMs)/002. Write a Gaussian Mixture Model in Python Code.en.srt
26 kB
Chapter 6 Gaussian Mixture Models (GMMs)/002. Write a Gaussian Mixture Model in Python Code.mp4
63 MB
Chapter 6 Gaussian Mixture Models (GMMs)/003. Practical Issues with GMM.en.srt
14 kB
Chapter 6 Gaussian Mixture Models (GMMs)/003. Practical Issues with GMM.mp4
19 MB
Chapter 6 Gaussian Mixture Models (GMMs)/004. Comparison between GMM and K-Means.en.srt
5.4 kB
Chapter 6 Gaussian Mixture Models (GMMs)/004. Comparison between GMM and K-Means.mp4
9.0 MB
Chapter 6 Gaussian Mixture Models (GMMs)/005. Kernel Density Estimation.en.srt
9.1 kB
Chapter 6 Gaussian Mixture Models (GMMs)/005. Kernel Density Estimation.mp4
14 MB
Chapter 6 Gaussian Mixture Models (GMMs)/006. GMM vs Bayes Classifier (pt 1).en.srt
13 kB
Chapter 6 Gaussian Mixture Models (GMMs)/006. GMM vs Bayes Classifier (pt 1).mp4
20 MB
Chapter 6 Gaussian Mixture Models (GMMs)/007. GMM vs Bayes Classifier (pt 2).en.srt
16 kB
Chapter 6 Gaussian Mixture Models (GMMs)/007. GMM vs Bayes Classifier (pt 2).mp4
22 MB
Chapter 6 Gaussian Mixture Models (GMMs)/008. Expectation-Maximization (pt 1).en.srt
16 kB
Chapter 6 Gaussian Mixture Models (GMMs)/008. Expectation-Maximization (pt 1).mp4
23 MB
Chapter 6 Gaussian Mixture Models (GMMs)/009. Expectation-Maximization (pt 2).en.srt
2.9 kB
Chapter 6 Gaussian Mixture Models (GMMs)/009. Expectation-Maximization (pt 2).mp4
5.0 MB
Chapter 6 Gaussian Mixture Models (GMMs)/010. Expectation-Maximization (pt 3).en.srt
11 kB
Chapter 6 Gaussian Mixture Models (GMMs)/010. Expectation-Maximization (pt 3).mp4
15 MB
Chapter 7 Setting Up Your Environment (Appendix)/001. Pre-Installation Check.en.srt
6.9 kB
Chapter 7 Setting Up Your Environment (Appendix)/001. Pre-Installation Check.mp4
11 MB
Chapter 7 Setting Up Your Environment (Appendix)/002. Anaconda Environment Setup.en.srt
22 kB
Chapter 2 Getting Set Up/001. Where to get the code.en.srt
6.3 kB
Chapter 7 Setting Up Your Environment (Appendix)/003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.en.srt
16 kB
Chapter 7 Setting Up Your Environment (Appendix)/003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.mp4
49 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/001. How to Code Yourself (part 1).en.srt
23 kB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/001. How to Code Yourself (part 1).mp4
30 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/002. How to Code Yourself (part 2).en.srt
14 kB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/002. How to Code Yourself (part 2).mp4
19 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/003. Proof that using Jupyter Notebook is the same as not using it.en.srt
16 kB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/003. Proof that using Jupyter Notebook is the same as not using it.mp4
34 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/004. How to use Github & Extra Coding Tips (Optional).en.srt
17 kB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/004. How to use Github & Extra Coding Tips (Optional).mp4
29 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/001. How to Succeed in this Course (Long Version).en.srt
16 kB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/001. How to Succeed in this Course (Long Version).mp4
17 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.en.srt
34 kB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
42 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/003. What order should I take your courses in (part 1).en.srt
18 kB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/003. What order should I take your courses in (part 1).mp4
28 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/004. What order should I take your courses in (part 2).en.srt
25 kB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/004. What order should I take your courses in (part 2).mp4
38 MB
z.9781836649373_Code/.gitignore
65 B
z.9781836649373_Code/best_fit_line.py
1.1 kB
z.9781836649373_Code/README.md
6.8 kB
z.9781836649373_Code/ab_testing/advertisement_clicks.csv
7.8 kB
z.9781836649373_Code/ab_testing/bayesian_bandit.py
1.9 kB
z.9781836649373_Code/ab_testing/bayesian_normal.py
2.1 kB
z.9781836649373_Code/ab_testing/bayesian_starter.py
1.9 kB
z.9781836649373_Code/ab_testing/cdfs_and_percentiles.py
639 B
z.9781836649373_Code/ab_testing/chisquare.py
1.9 kB
z.9781836649373_Code/ab_testing/ci_comparison.py
1.2 kB
z.9781836649373_Code/ab_testing/client.py
1.3 kB
z.9781836649373_Code/ab_testing/comparing_epsilons.py
2.1 kB
z.9781836649373_Code/ab_testing/convergence.py
1.0 kB
z.9781836649373_Code/ab_testing/demo.py
982 B
z.9781836649373_Code/ab_testing/epsilon_greedy.py
2.4 kB
z.9781836649373_Code/ab_testing/epsilon_greedy_starter.py
2.2 kB
z.9781836649373_Code/ab_testing/ex_chisq.py
1.2 kB
z.9781836649373_Code/ab_testing/ex_ttest.py
1.2 kB
z.9781836649373_Code/ab_testing/extra_reading.txt
792 B
z.9781836649373_Code/ab_testing/optimistic.py
1.9 kB
z.9781836649373_Code/ab_testing/optimistic_starter.py
1.8 kB
z.9781836649373_Code/ab_testing/server_solution.py
1.7 kB
z.9781836649373_Code/ab_testing/server_starter.py
1.3 kB
z.9781836649373_Code/ab_testing/ttest.py
1020 B
z.9781836649373_Code/ab_testing/ucb1.py
2.1 kB
z.9781836649373_Code/ab_testing/ucb1_starter.py
2.1 kB
z.9781836649373_Code/airline/ann.py
5.4 kB
z.9781836649373_Code/airline/international-airline-passengers.csv
2.3 kB
z.9781836649373_Code/airline/lr.py
1.8 kB
z.9781836649373_Code/airline/rnn.py
5.0 kB
z.9781836649373_Code/ann_class/backprop.py
4.1 kB
z.9781836649373_Code/ann_class/extra_reading.txt
485 B
z.9781836649373_Code/ann_class/forwardprop.py
1.8 kB
z.9781836649373_Code/ann_class/regression.py
3.0 kB
z.9781836649373_Code/ann_class/sklearn_ann.py
961 B
z.9781836649373_Code/ann_class/tf_example.py
2.4 kB
z.9781836649373_Code/ann_class/xor_donut.py
4.4 kB
Chapter 1 Welcome/001. Introduction.en.srt
7.5 kB
z.9781836649373_Code/ann_class2/adam.py
5.9 kB
z.9781836649373_Code/ann_class2/batch_norm_tf.py
5.6 kB
z.9781836649373_Code/ann_class2/batch_norm_theano.py
5.8 kB
z.9781836649373_Code/ann_class2/cntk_example.py
3.2 kB
z.9781836649373_Code/ann_class2/dropout_tensorflow.py
4.9 kB
z.9781836649373_Code/ann_class2/dropout_theano.py
5.0 kB
z.9781836649373_Code/ann_class2/extra_reading.txt
1.2 kB
z.9781836649373_Code/ann_class2/grid_search.py
2.5 kB
z.9781836649373_Code/ann_class2/keras_example.py
2.3 kB
z.9781836649373_Code/ann_class2/keras_functional.py
2.2 kB
z.9781836649373_Code/ann_class2/mlp.py
1.2 kB
z.9781836649373_Code/ann_class2/momentum.py
6.2 kB
z.9781836649373_Code/ann_class2/mxnet_example.py
2.6 kB
z.9781836649373_Code/ann_class2/pytorch_batchnorm.py
4.9 kB
z.9781836649373_Code/ann_class2/pytorch_dropout.py
5.0 kB
z.9781836649373_Code/ann_class2/pytorch_example.py
3.7 kB
z.9781836649373_Code/ann_class2/pytorch_example2.py
4.5 kB
z.9781836649373_Code/ann_class2/random_search.py
2.3 kB
z.9781836649373_Code/ann_class2/rmsprop.py
4.6 kB
z.9781836649373_Code/ann_class2/rmsprop_test.py
2.9 kB
z.9781836649373_Code/ann_class2/sgd.py
5.3 kB
z.9781836649373_Code/ann_class2/tensorflow1.py
3.1 kB
z.9781836649373_Code/ann_class2/tensorflow2.py
3.7 kB
z.9781836649373_Code/ann_class2/tf_with_save.py
4.2 kB
z.9781836649373_Code/ann_class2/theano_ann.py
3.8 kB
z.9781836649373_Code/ann_class2/theano1.py
1.8 kB
z.9781836649373_Code/ann_class2/theano2.py
3.5 kB
z.9781836649373_Code/ann_class2/util.py
7.9 kB
Chapter 1 Welcome/001. Introduction.mp4
14 MB
z.9781836649373_Code/ann_logistic_extra/ann_predict.py
867 B
z.9781836649373_Code/ann_logistic_extra/ann_train.py
2.2 kB
z.9781836649373_Code/ann_logistic_extra/ecommerce_data.csv
12 kB
z.9781836649373_Code/ann_logistic_extra/logistic_predict.py
663 B
z.9781836649373_Code/ann_logistic_extra/logistic_softmax_train.py
1.8 kB
z.9781836649373_Code/ann_logistic_extra/logistic_train.py
1.5 kB
z.9781836649373_Code/ann_logistic_extra/process.py
1.8 kB
z.9781836649373_Code/bayesian_ml/1/nb.py
4.3 kB
z.9781836649373_Code/bayesian_ml/1/Q.csv
119 kB
z.9781836649373_Code/bayesian_ml/1/README
822 B
z.9781836649373_Code/bayesian_ml/1/Xtest.csv
235 kB
z.9781836649373_Code/bayesian_ml/1/Xtrain.csv
1.4 MB
z.9781836649373_Code/bayesian_ml/1/ytest.csv
3.9 kB
z.9781836649373_Code/bayesian_ml/1/ytrain.csv
23 kB
z.9781836649373_Code/bayesian_ml/2/em.py
1.6 kB
z.9781836649373_Code/bayesian_ml/2/probit.py
3.6 kB
z.9781836649373_Code/bayesian_ml/2/Q.csv
119 kB
z.9781836649373_Code/bayesian_ml/2/README
822 B
z.9781836649373_Code/bayesian_ml/2/Xtest.csv
235 kB
z.9781836649373_Code/bayesian_ml/2/Xtrain.csv
1.4 MB
z.9781836649373_Code/bayesian_ml/2/ytest.csv
3.9 kB
z.9781836649373_Code/bayesian_ml/2/ytrain.csv
23 kB
z.9781836649373_Code/bayesian_ml/3/run.py
3.9 kB
z.9781836649373_Code/bayesian_ml/3/X_set1.csv
95 kB
z.9781836649373_Code/bayesian_ml/3/X_set2.csv
596 kB
z.9781836649373_Code/bayesian_ml/3/X_set3.csv
2.3 MB
z.9781836649373_Code/bayesian_ml/3/y_set1.csv
784 B
z.9781836649373_Code/bayesian_ml/3/y_set2.csv
1.9 kB
z.9781836649373_Code/bayesian_ml/3/y_set3.csv
3.8 kB
z.9781836649373_Code/bayesian_ml/3/z_set1.csv
772 B
z.9781836649373_Code/bayesian_ml/3/z_set2.csv
1.9 kB
z.9781836649373_Code/bayesian_ml/3/z_set3.csv
3.7 kB
z.9781836649373_Code/bayesian_ml/4/data.txt
3.7 kB
z.9781836649373_Code/bayesian_ml/4/emgmm.py
1.9 kB
z.9781836649373_Code/bayesian_ml/4/npbgmm.py
6.4 kB
z.9781836649373_Code/bayesian_ml/4/vigmm.py
6.2 kB
z.9781836649373_Code/calculus/extra_reading.txt
55 B
z.9781836649373_Code/calculus/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/chatgpt_trading/extra_reading.txt
149 B
z.9781836649373_Code/chatgpt_trading/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/cnn_class/benchmark.py
4.5 kB
z.9781836649373_Code/cnn_class/blur.py
1.6 kB
z.9781836649373_Code/cnn_class/cifar.py
7.0 kB
z.9781836649373_Code/cnn_class/cnn_tf.py
7.1 kB
z.9781836649373_Code/cnn_class/cnn_tf_plot_filters.py
6.7 kB
z.9781836649373_Code/cnn_class/cnn_theano.py
5.6 kB
z.9781836649373_Code/cnn_class/cnn_theano_plot_filters.py
6.8 kB
z.9781836649373_Code/cnn_class/custom_blur.py
2.7 kB
z.9781836649373_Code/cnn_class/echo.py
1.6 kB
z.9781836649373_Code/cnn_class/edge.py
1.1 kB
z.9781836649373_Code/cnn_class/edge_benchmark.py
4.0 kB
z.9781836649373_Code/cnn_class/exercises.txt
760 B
z.9781836649373_Code/cnn_class/extra_reading.txt
695 B
z.9781836649373_Code/cnn_class/helloworld.wav
36 kB
z.9781836649373_Code/cnn_class/keras_example.py
2.8 kB
z.9781836649373_Code/cnn_class/lena.png
463 kB
z.9781836649373_Code/cnn_class/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/cnn_class2/class_activation_maps.py
2.3 kB
z.9781836649373_Code/cnn_class2/extra_reading.txt
526 B
z.9781836649373_Code/cnn_class2/fashion.py
2.7 kB
z.9781836649373_Code/cnn_class2/fashion2.py
2.4 kB
z.9781836649373_Code/cnn_class2/make_limited_datasets.py
928 B
z.9781836649373_Code/cnn_class2/siamese.py
12 kB
z.9781836649373_Code/cnn_class2/ssd.py
4.5 kB
z.9781836649373_Code/cnn_class2/style_transfer1.py
4.7 kB
z.9781836649373_Code/cnn_class2/style_transfer2.py
4.0 kB
z.9781836649373_Code/cnn_class2/style_transfer3.py
3.8 kB
z.9781836649373_Code/cnn_class2/test_softmax.py
832 B
z.9781836649373_Code/cnn_class2/tf_resnet.py
7.5 kB
z.9781836649373_Code/cnn_class2/tf_resnet_convblock.py
6.1 kB
z.9781836649373_Code/cnn_class2/tf_resnet_convblock_starter.py
765 B
z.9781836649373_Code/cnn_class2/tf_resnet_first_layers.py
4.5 kB
z.9781836649373_Code/cnn_class2/tf_resnet_first_layers_starter.py
2.3 kB
z.9781836649373_Code/cnn_class2/tf_resnet_identity_block.py
3.6 kB
z.9781836649373_Code/cnn_class2/tf_resnet_identity_block_starter.py
872 B
z.9781836649373_Code/cnn_class2/use_pretrained_weights_resnet.py
4.7 kB
z.9781836649373_Code/cnn_class2/use_pretrained_weights_vgg.py
4.7 kB
z.9781836649373_Code/cnn_class2/util.py
1.5 kB
z.9781836649373_Code/cnn_class2/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/cnn_class2/content/elephant.jpg
22 kB
z.9781836649373_Code/cnn_class2/content/sydney.jpg
79 kB
z.9781836649373_Code/cnn_class2/styles/flowercarrier.jpg
93 kB
z.9781836649373_Code/cnn_class2/styles/lesdemoisellesdavignon.jpg
177 kB
z.9781836649373_Code/cnn_class2/styles/monalisa.jpg
226 kB
z.9781836649373_Code/cnn_class2/styles/starrynight.jpg
34 kB
z.9781836649373_Code/data_csv/legend.txt
6.6 kB
z.9781836649373_Code/data_csv/readme.txt
590 B
z.9781836649373_Code/data_csv/X.txt
20 kB
z.9781836649373_Code/data_csv/X_orig.txt
9.0 kB
z.9781836649373_Code/data_csv/y.txt
1.4 kB
z.9781836649373_Code/financial_engineering/go_here_instead.txt
56 B
Chapter 1 Welcome/002. Course Outline.en.srt
6.5 kB
z.9781836649373_Code/hmm_class/coin_data.txt
1.5 kB
z.9781836649373_Code/hmm_class/edgar_allan_poe.txt
26 kB
z.9781836649373_Code/hmm_class/extra_reading.txt
396 B
z.9781836649373_Code/hmm_class/frost.py
3.1 kB
z.9781836649373_Code/hmm_class/generate_c.py
2.2 kB
z.9781836649373_Code/hmm_class/generate_ht.py
1.2 kB
z.9781836649373_Code/hmm_class/helloworld.wav
36 kB
z.9781836649373_Code/hmm_class/hmm_classifier.py
3.0 kB
z.9781836649373_Code/hmm_class/hmmc.py
9.7 kB
z.9781836649373_Code/hmm_class/hmmc_concat.py
8.6 kB
z.9781836649373_Code/hmm_class/hmmc_scaled_concat.py
8.8 kB
z.9781836649373_Code/hmm_class/hmmc_scaled_concat_diag.py
10 kB
z.9781836649373_Code/hmm_class/hmmc_tf.py
7.7 kB
z.9781836649373_Code/hmm_class/hmmc_theano.py
6.3 kB
z.9781836649373_Code/hmm_class/hmmc_theano2.py
6.9 kB
z.9781836649373_Code/hmm_class/hmmd.py
7.1 kB
z.9781836649373_Code/hmm_class/hmmd_scaled.py
6.1 kB
z.9781836649373_Code/hmm_class/hmmd_tf.py
4.7 kB
z.9781836649373_Code/hmm_class/hmmd_theano.py
4.5 kB
z.9781836649373_Code/hmm_class/hmmd_theano2.py
4.7 kB
z.9781836649373_Code/hmm_class/robert_frost.txt
55 kB
z.9781836649373_Code/hmm_class/scan1.py
732 B
z.9781836649373_Code/hmm_class/scan2.py
773 B
z.9781836649373_Code/hmm_class/scan3.py
1023 B
z.9781836649373_Code/hmm_class/site_data.csv
410 kB
z.9781836649373_Code/hmm_class/sites.py
973 B
z.9781836649373_Code/hmm_class/tf_scan1.py
948 B
z.9781836649373_Code/hmm_class/tf_scan2.py
949 B
z.9781836649373_Code/hmm_class/tf_scan3.py
1.2 kB
z.9781836649373_Code/keras_examples/ann.py
1.7 kB
z.9781836649373_Code/keras_examples/basic_mlp.py
1.0 kB
z.9781836649373_Code/keras_examples/batchnorm.py
1.8 kB
z.9781836649373_Code/keras_examples/cnn.py
2.0 kB
z.9781836649373_Code/keras_examples/cnn_cifar.py
2.2 kB
z.9781836649373_Code/keras_examples/cnn_dropout_batchnorm.py
2.1 kB
z.9781836649373_Code/keras_examples/dropout.py
1.8 kB
z.9781836649373_Code/keras_examples/sentiment_analysis.py
2.6 kB
z.9781836649373_Code/keras_examples/sine.py
1.4 kB
z.9781836649373_Code/keras_examples/sine2.py
1.4 kB
z.9781836649373_Code/keras_examples/translation.py
4.0 kB
z.9781836649373_Code/keras_examples/util.py
2.1 kB
z.9781836649373_Code/kerascv/extra_reading.txt
210 B
z.9781836649373_Code/kerascv/imagenet_label_names.json
14 kB
z.9781836649373_Code/kerascv/makelist.py
199 B
z.9781836649373_Code/kerascv/pascal2coco.py
5.4 kB
z.9781836649373_Code/linear_algebra/extra_reading.txt
291 B
z.9781836649373_Code/linear_algebra/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/linear_regression_class/data_1d.csv
2.7 kB
z.9781836649373_Code/linear_regression_class/data_2d.csv
4.1 kB
z.9781836649373_Code/linear_regression_class/data_poly.csv
2.7 kB
z.9781836649373_Code/linear_regression_class/gd.py
323 B
z.9781836649373_Code/linear_regression_class/generate_1d.py
642 B
z.9781836649373_Code/linear_regression_class/generate_2d.py
688 B
z.9781836649373_Code/linear_regression_class/generate_poly.py
675 B
z.9781836649373_Code/linear_regression_class/gradient_descent.py
1.1 kB
z.9781836649373_Code/linear_regression_class/l1_regularization.py
1.3 kB
z.9781836649373_Code/linear_regression_class/l2_regularization.py
1.2 kB
z.9781836649373_Code/linear_regression_class/lr_1d.py
1.4 kB
z.9781836649373_Code/linear_regression_class/lr_2d.py
1.4 kB
z.9781836649373_Code/linear_regression_class/lr_poly.py
1.8 kB
z.9781836649373_Code/linear_regression_class/mlr02.xls
751 B
z.9781836649373_Code/linear_regression_class/moore.csv
5.7 kB
z.9781836649373_Code/linear_regression_class/moore.py
1.8 kB
z.9781836649373_Code/linear_regression_class/overfitting.py
2.4 kB
z.9781836649373_Code/linear_regression_class/systolic.py
1.4 kB
z.9781836649373_Code/logistic_regression_class/bad_xor.py
1.5 kB
z.9781836649373_Code/logistic_regression_class/l1_regularization.py
1.6 kB
z.9781836649373_Code/logistic_regression_class/logistic_donut.py
2.0 kB
z.9781836649373_Code/logistic_regression_class/logistic_visualize.py
1.1 kB
z.9781836649373_Code/logistic_regression_class/logistic_xor.py
1.6 kB
z.9781836649373_Code/logistic_regression_class/logistic1.py
791 B
z.9781836649373_Code/logistic_regression_class/logistic2.py
1.4 kB
z.9781836649373_Code/logistic_regression_class/logistic3.py
1.8 kB
z.9781836649373_Code/logistic_regression_class/logistic4.py
1.5 kB
z.9781836649373_Code/matrix_calculus/extra_reading.txt
78 B
z.9781836649373_Code/mnist_csv/label_test.txt
1000 B
z.9781836649373_Code/mnist_csv/label_train.txt
9.8 kB
z.9781836649373_Code/mnist_csv/Q.txt
158 kB
z.9781836649373_Code/mnist_csv/Xtest.txt
81 kB
z.9781836649373_Code/mnist_csv/Xtrain.txt
806 kB
z.9781836649373_Code/naive_bayes/extra_reading.txt
285 B
z.9781836649373_Code/naive_bayes/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/nlp_class/all_book_titles.txt
125 kB
z.9781836649373_Code/nlp_class/article_spinner.py
2.5 kB
z.9781836649373_Code/nlp_class/cipher_placeholder.py
976 B
z.9781836649373_Code/nlp_class/extra_reading.txt
745 B
z.9781836649373_Code/nlp_class/lsa.py
3.4 kB
z.9781836649373_Code/nlp_class/nb.py
1.3 kB
z.9781836649373_Code/nlp_class/sentiment.py
5.6 kB
z.9781836649373_Code/nlp_class/spam2.py
2.5 kB
z.9781836649373_Code/nlp_class/spambase.data
686 kB
z.9781836649373_Code/nlp_class/stopwords.txt
2.4 kB
z.9781836649373_Code/nlp_class/electronics/negative.review
1.1 MB
z.9781836649373_Code/nlp_class/electronics/positive.review
1.1 MB
z.9781836649373_Code/nlp_class/electronics/unlabeled.review
14 MB
z.9781836649373_Code/nlp_class2/bow_classifier.py
3.9 kB
z.9781836649373_Code/nlp_class2/extra_reading.txt
713 B
z.9781836649373_Code/nlp_class2/glove.py
12 kB
z.9781836649373_Code/nlp_class2/glove_svd.py
6.6 kB
z.9781836649373_Code/nlp_class2/glove_tf.py
8.0 kB
z.9781836649373_Code/nlp_class2/glove_theano.py
8.4 kB
z.9781836649373_Code/nlp_class2/logistic.py
3.8 kB
z.9781836649373_Code/nlp_class2/markov.py
4.1 kB
z.9781836649373_Code/nlp_class2/ner.txt
348 kB
z.9781836649373_Code/nlp_class2/ner_baseline.py
3.7 kB
z.9781836649373_Code/nlp_class2/ner_rnn.py
874 B
z.9781836649373_Code/nlp_class2/ner_tf.py
5.8 kB
z.9781836649373_Code/nlp_class2/neural_network.py
4.0 kB
z.9781836649373_Code/nlp_class2/neural_network2.py
4.7 kB
z.9781836649373_Code/nlp_class2/pmi.py
9.0 kB
z.9781836649373_Code/nlp_class2/pos_baseline.py
6.9 kB
z.9781836649373_Code/nlp_class2/pos_hmm.py
2.4 kB
z.9781836649373_Code/nlp_class2/pos_ner_keras.py
5.6 kB
z.9781836649373_Code/nlp_class2/pos_rnn.py
5.0 kB
z.9781836649373_Code/nlp_class2/pos_tf.py
6.6 kB
z.9781836649373_Code/nlp_class2/pretrained_glove.py
4.0 kB
z.9781836649373_Code/nlp_class2/pretrained_w2v.py
2.7 kB
z.9781836649373_Code/nlp_class2/recursive_tensorflow.py
7.0 kB
z.9781836649373_Code/nlp_class2/recursive_theano.py
9.7 kB
z.9781836649373_Code/nlp_class2/rntn_tensorflow.py
7.8 kB
z.9781836649373_Code/nlp_class2/rntn_tensorflow_rnn.py
11 kB
z.9781836649373_Code/nlp_class2/rntn_theano.py
11 kB
z.9781836649373_Code/nlp_class2/tfidf_tsne.py
3.1 kB
z.9781836649373_Code/nlp_class2/util.py
6.4 kB
z.9781836649373_Code/nlp_class2/visualize_countries.py
1.1 kB
z.9781836649373_Code/nlp_class2/w2v_model.npz
157 kB
z.9781836649373_Code/nlp_class2/w2v_word2idx.json
31 kB
z.9781836649373_Code/nlp_class2/word2vec.py
10 kB
z.9781836649373_Code/nlp_class2/word2vec_tf.py
13 kB
z.9781836649373_Code/nlp_class2/word2vec_theano.py
12 kB
z.9781836649373_Code/nlp_class3/attention.py
13 kB
z.9781836649373_Code/nlp_class3/bilstm_mnist.py
2.6 kB
z.9781836649373_Code/nlp_class3/bilstm_test.py
997 B
z.9781836649373_Code/nlp_class3/cnn_toxic.py
4.3 kB
z.9781836649373_Code/nlp_class3/convert_twitter.py
706 B
z.9781836649373_Code/nlp_class3/extra_reading.txt
1.4 kB
z.9781836649373_Code/nlp_class3/lstm_toxic.py
4.1 kB
z.9781836649373_Code/nlp_class3/memory_network.py
12 kB
z.9781836649373_Code/nlp_class3/poetry.py
6.0 kB
z.9781836649373_Code/nlp_class3/simple_rnn_test.py
1.6 kB
z.9781836649373_Code/nlp_class3/wseq2seq.py
10 kB
z.9781836649373_Code/nlp_v2/extra_reading.txt
1.5 kB
z.9781836649373_Code/nlp_v2/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/numpy_class/classification_example.py
2.5 kB
z.9781836649373_Code/numpy_class/dot_for.py
605 B
z.9781836649373_Code/numpy_class/manual_data_loading.py
549 B
z.9781836649373_Code/numpy_class/regression_example.py
2.6 kB
z.9781836649373_Code/numpy_class/table1.csv
78 B
z.9781836649373_Code/numpy_class/table2.csv
85 B
z.9781836649373_Code/numpy_class/exercises/ex1.py
694 B
z.9781836649373_Code/numpy_class/exercises/ex2.py
696 B
z.9781836649373_Code/numpy_class/exercises/ex3.py
824 B
z.9781836649373_Code/numpy_class/exercises/ex4.py
1.1 kB
z.9781836649373_Code/numpy_class/exercises/ex5.py
1.1 kB
z.9781836649373_Code/numpy_class/exercises/ex6.py
652 B
z.9781836649373_Code/numpy_class/exercises/ex7.py
1.0 kB
z.9781836649373_Code/numpy_class/exercises/ex8.py
1.4 kB
z.9781836649373_Code/numpy_class/exercises/ex9.py
789 B
z.9781836649373_Code/numpy_class/python3/dot_for.py
772 B
z.9781836649373_Code/numpy_class/python3/manual_data_loading.py
559 B
z.9781836649373_Code/openai/extra_reading.txt
1020 B
z.9781836649373_Code/openai/fight.mp4
602 kB
z.9781836649373_Code/openai/finance.png
73 kB
z.9781836649373_Code/openai/handwriting.jpg
52 kB
z.9781836649373_Code/openai/physics_problem.jpeg
25 kB
z.9781836649373_Code/openai/replies.json
26 kB
z.9781836649373_Code/openai/robots_playing_soccer.jpeg
137 kB
z.9781836649373_Code/openai/webdesign.jpg
49 kB
z.9781836649373_Code/prophet/extra_reading.txt
76 B
z.9781836649373_Code/pytorch/.gitignore
43 B
z.9781836649373_Code/pytorch/aapl_msi_sbux.csv
24 kB
z.9781836649373_Code/pytorch/ann_regression.py
2.5 kB
z.9781836649373_Code/pytorch/exercises.txt
1.1 kB
z.9781836649373_Code/pytorch/extra_reading.txt
1.1 kB
z.9781836649373_Code/pytorch/plot_rl_rewards.py
548 B
z.9781836649373_Code/pytorch/rl_trader.py
12 kB
z.9781836649373_Code/pytorch/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/recommenders/autorec.py
3.0 kB
z.9781836649373_Code/recommenders/extra_reading.txt
2.1 kB
z.9781836649373_Code/recommenders/itembased.py
5.0 kB
z.9781836649373_Code/recommenders/mf.py
3.6 kB
z.9781836649373_Code/recommenders/mf_keras.py
2.6 kB
z.9781836649373_Code/recommenders/mf_keras_deep.py
2.3 kB
z.9781836649373_Code/recommenders/mf_keras_res.py
2.4 kB
z.9781836649373_Code/recommenders/mf2.py
4.2 kB
z.9781836649373_Code/recommenders/preprocess.py
1.1 kB
z.9781836649373_Code/recommenders/preprocess_shrink.py
1.8 kB
z.9781836649373_Code/recommenders/preprocess2dict.py
2.2 kB
z.9781836649373_Code/recommenders/preprocess2sparse.py
1.6 kB
z.9781836649373_Code/recommenders/rbm_tf_k.py
8.3 kB
z.9781836649373_Code/recommenders/rbm_tf_k_faster.py
7.2 kB
z.9781836649373_Code/recommenders/spark.py
1.5 kB
z.9781836649373_Code/recommenders/spark2.py
1.8 kB
z.9781836649373_Code/recommenders/tfidf.py
2.0 kB
z.9781836649373_Code/recommenders/userbased.py
5.0 kB
z.9781836649373_Code/rl/approx_control.py
4.2 kB
z.9781836649373_Code/rl/approx_prediction.py
3.3 kB
z.9781836649373_Code/rl/bayesian_bandit.py
1.9 kB
z.9781836649373_Code/rl/bayesian_normal.py
2.1 kB
z.9781836649373_Code/rl/bayesian_starter.py
1.9 kB
z.9781836649373_Code/rl/cartpole.py
3.9 kB
z.9781836649373_Code/rl/cartpole_gym0.19.py
3.6 kB
z.9781836649373_Code/rl/comparing_epsilons.py
1.8 kB
z.9781836649373_Code/rl/comparing_explore_exploit_methods.py
3.0 kB
z.9781836649373_Code/rl/epsilon_greedy.py
2.3 kB
z.9781836649373_Code/rl/epsilon_greedy_starter.py
2.2 kB
z.9781836649373_Code/rl/extra_reading.txt
1.8 kB
z.9781836649373_Code/rl/grid_world.py
9.5 kB
z.9781836649373_Code/rl/iterative_policy_evaluation_deterministic.py
3.0 kB
z.9781836649373_Code/rl/iterative_policy_evaluation_probabilistic.py
3.1 kB
z.9781836649373_Code/rl/linear_rl_trader.py
9.6 kB
z.9781836649373_Code/rl/monte_carlo.py
2.7 kB
z.9781836649373_Code/rl/monte_carlo_es.py
4.1 kB
z.9781836649373_Code/rl/monte_carlo_no_es.py
4.3 kB
z.9781836649373_Code/rl/optimistic.py
1.9 kB
z.9781836649373_Code/rl/optimistic_initial_values.py
1.7 kB
z.9781836649373_Code/rl/optimistic_starter.py
1.8 kB
z.9781836649373_Code/rl/plot_rl_rewards.py
555 B
z.9781836649373_Code/rl/policy_iteration_deterministic.py
4.1 kB
z.9781836649373_Code/rl/policy_iteration_probabilistic.py
4.0 kB
z.9781836649373_Code/rl/q_learning.py
2.5 kB
z.9781836649373_Code/rl/sarsa.py
2.6 kB
z.9781836649373_Code/rl/td0_prediction.py
2.1 kB
z.9781836649373_Code/rl/tic_tac_toe.py
13 kB
z.9781836649373_Code/rl/ucb1.py
2.1 kB
z.9781836649373_Code/rl/ucb1_starter.py
2.1 kB
z.9781836649373_Code/rl/value_iteration.py
3.0 kB
z.9781836649373_Code/rl2/extra_reading.txt
1.1 kB
z.9781836649373_Code/rl2/gym_tutorial.py
1.5 kB
z.9781836649373_Code/rl2/a3c/main.py
2.4 kB
z.9781836649373_Code/rl2/a3c/nets.py
4.3 kB
z.9781836649373_Code/rl2/a3c/thread_example.py
982 B
z.9781836649373_Code/rl2/a3c/worker.py
8.8 kB
z.9781836649373_Code/rl2/atari/dqn_tf.py
13 kB
z.9781836649373_Code/rl2/atari/dqn_theano.py
14 kB
z.9781836649373_Code/rl2/cartpole/dqn_tf.py
6.8 kB
z.9781836649373_Code/rl2/cartpole/dqn_theano.py
6.8 kB
z.9781836649373_Code/rl2/cartpole/pg_tf.py
7.7 kB
z.9781836649373_Code/rl2/cartpole/pg_theano.py
7.1 kB
z.9781836649373_Code/rl2/cartpole/q_learning.py
4.6 kB
z.9781836649373_Code/rl2/cartpole/q_learning_bins.py
4.0 kB
z.9781836649373_Code/rl2/cartpole/random_search.py
1.7 kB
z.9781836649373_Code/rl2/cartpole/save_a_video.py
1.7 kB
z.9781836649373_Code/rl2/cartpole/td_lambda.py
4.3 kB
z.9781836649373_Code/rl2/cartpole/tf_warmup.py
1.5 kB
z.9781836649373_Code/rl2/cartpole/theano_warmup.py
1.0 kB
z.9781836649373_Code/rl2/mountaincar/n_step.py
4.7 kB
z.9781836649373_Code/rl2/mountaincar/pg_tf.py
6.5 kB
z.9781836649373_Code/rl2/mountaincar/pg_tf_random.py
7.2 kB
z.9781836649373_Code/rl2/mountaincar/pg_theano.py
7.0 kB
z.9781836649373_Code/rl2/mountaincar/pg_theano_random.py
6.0 kB
z.9781836649373_Code/rl2/mountaincar/q_learning.py
6.4 kB
z.9781836649373_Code/rl2/mountaincar/td_lambda.py
3.8 kB
z.9781836649373_Code/rl3/ddpg.py
11 kB
z.9781836649373_Code/rl3/es_flappy.py
6.0 kB
z.9781836649373_Code/rl3/es_mnist.py
3.6 kB
z.9781836649373_Code/rl3/es_mujoco.py
4.7 kB
z.9781836649373_Code/rl3/es_simple.py
1.3 kB
z.9781836649373_Code/rl3/extra_reading.txt
851 B
z.9781836649373_Code/rl3/flappy2envs.py
4.2 kB
z.9781836649373_Code/rl3/gym_review.py
1.4 kB
z.9781836649373_Code/rl3/plot_ddpg_result.py
746 B
z.9781836649373_Code/rl3/plot_es_flappy_results.py
452 B
z.9781836649373_Code/rl3/plot_es_mujoco_results.py
452 B
z.9781836649373_Code/rl3/sample_test.py
350 B
z.9781836649373_Code/rl3/a2c/a2c.py
8.5 kB
z.9781836649373_Code/rl3/a2c/atari_wrappers.py
9.9 kB
z.9781836649373_Code/rl3/a2c/main.py
1.6 kB
z.9781836649373_Code/rl3/a2c/neural_network.py
1.8 kB
z.9781836649373_Code/rl3/a2c/play.py
1.8 kB
z.9781836649373_Code/rl3/a2c/subproc_vec_env.py
3.4 kB
Chapter 1 Welcome/002. Course Outline.mp4
9.5 MB
z.9781836649373_Code/rnn_class/batch_gru.py
2.4 kB
z.9781836649373_Code/rnn_class/batch_parity.py
5.7 kB
z.9781836649373_Code/rnn_class/batch_units.py
6.7 kB
z.9781836649373_Code/rnn_class/batch_wiki.py
6.8 kB
z.9781836649373_Code/rnn_class/brown.py
2.9 kB
z.9781836649373_Code/rnn_class/exercises.txt
636 B
z.9781836649373_Code/rnn_class/extra_reading.txt
126 B
z.9781836649373_Code/rnn_class/gru.py
1.9 kB
z.9781836649373_Code/rnn_class/gru_nonorm_part1_wikipedia_word2idx.json
30 kB
z.9781836649373_Code/rnn_class/gru_nonorm_part1_word_embeddings.npy
1.2 MB
z.9781836649373_Code/rnn_class/lstm.py
2.9 kB
z.9781836649373_Code/rnn_class/mlp_parity.py
4.0 kB
z.9781836649373_Code/rnn_class/poetry_classifier.py
4.8 kB
z.9781836649373_Code/rnn_class/rrnn_language.py
6.7 kB
z.9781836649373_Code/rnn_class/srn_language.py
7.5 kB
z.9781836649373_Code/rnn_class/srn_language_tf.py
7.6 kB
z.9781836649373_Code/rnn_class/srn_parity.py
3.4 kB
z.9781836649373_Code/rnn_class/srn_parity_tf.py
3.3 kB
z.9781836649373_Code/rnn_class/tf_parity.py
4.4 kB
z.9781836649373_Code/rnn_class/util.py
8.8 kB
z.9781836649373_Code/rnn_class/visualize_embeddings.py
1.2 kB
z.9781836649373_Code/rnn_class/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/rnn_class/wiki.py
6.5 kB
z.9781836649373_Code/stats/extra_reading.txt
110 B
z.9781836649373_Code/supervised_class/app.py
1.4 kB
z.9781836649373_Code/supervised_class/app_caller.py
985 B
z.9781836649373_Code/supervised_class/app_trainer.py
830 B
z.9781836649373_Code/supervised_class/bayes.py
2.2 kB
z.9781836649373_Code/supervised_class/dt.py
6.7 kB
z.9781836649373_Code/supervised_class/dt_without_recursion.py
10 kB
z.9781836649373_Code/supervised_class/knn.py
3.1 kB
z.9781836649373_Code/supervised_class/knn_donut.py
657 B
z.9781836649373_Code/supervised_class/knn_fail.py
1.1 kB
z.9781836649373_Code/supervised_class/knn_vectorized.py
3.0 kB
z.9781836649373_Code/supervised_class/knn_xor.py
653 B
z.9781836649373_Code/supervised_class/multinomialnb.py
1.9 kB
z.9781836649373_Code/supervised_class/nb.py
2.0 kB
z.9781836649373_Code/supervised_class/perceptron.py
3.0 kB
z.9781836649373_Code/supervised_class/regression.py
856 B
z.9781836649373_Code/supervised_class/util.py
1.6 kB
z.9781836649373_Code/supervised_class2/adaboost.py
2.6 kB
z.9781836649373_Code/supervised_class2/bagging_classification.py
2.1 kB
z.9781836649373_Code/supervised_class2/bagging_regression.py
1.9 kB
z.9781836649373_Code/supervised_class2/bias_variance_demo.py
4.0 kB
z.9781836649373_Code/supervised_class2/bootstrap.py
1.4 kB
z.9781836649373_Code/supervised_class2/extra_reading.txt
545 B
z.9781836649373_Code/supervised_class2/knn_dt_demo.py
4.2 kB
z.9781836649373_Code/supervised_class2/rf_classification.py
3.6 kB
z.9781836649373_Code/supervised_class2/rf_regression.py
4.2 kB
z.9781836649373_Code/supervised_class2/rf_vs_bag.py
1.7 kB
z.9781836649373_Code/supervised_class2/rf_vs_bag2.py
2.8 kB
z.9781836649373_Code/supervised_class2/util.py
2.5 kB
z.9781836649373_Code/svm_class/crossval.py
885 B
z.9781836649373_Code/svm_class/extra_reading.txt
2.7 kB
z.9781836649373_Code/svm_class/fake_neural_net.py
4.1 kB
z.9781836649373_Code/svm_class/kernel_svm_gradient_primal.py
5.8 kB
z.9781836649373_Code/svm_class/linear_svm_gradient.py
4.4 kB
z.9781836649373_Code/svm_class/rbfnetwork.py
2.4 kB
z.9781836649373_Code/svm_class/real_neural_net.py
1.1 kB
z.9781836649373_Code/svm_class/regression.py
1.3 kB
z.9781836649373_Code/svm_class/svm_gradient.py
4.7 kB
z.9781836649373_Code/svm_class/svm_medical.py
1.0 kB
z.9781836649373_Code/svm_class/svm_mnist.py
830 B
z.9781836649373_Code/svm_class/svm_smo.py
9.3 kB
z.9781836649373_Code/svm_class/svm_spam.py
2.5 kB
z.9781836649373_Code/svm_class/util.py
4.3 kB
z.9781836649373_Code/tensorflow/input_data.py
5.7 kB
z.9781836649373_Code/tensorflow/MNIST_data/t10k-images-idx3-ubyte.gz
1.6 MB
z.9781836649373_Code/tensorflow/MNIST_data/t10k-labels-idx1-ubyte.gz
4.4 kB
z.9781836649373_Code/tensorflow/MNIST_data/train-images-idx3-ubyte.gz
9.5 MB
z.9781836649373_Code/tensorflow/MNIST_data/train-labels-idx1-ubyte.gz
28 kB
z.9781836649373_Code/tf2.0/.gitignore
59 B
z.9781836649373_Code/tf2.0/aapl_msi_sbux.csv
24 kB
z.9781836649373_Code/tf2.0/auto-mpg.data
30 kB
z.9781836649373_Code/tf2.0/daily-minimum-temperatures-in-me.csv
66 kB
z.9781836649373_Code/tf2.0/exercises.txt
1.1 kB
z.9781836649373_Code/tf2.0/extra_reading.txt
1.4 kB
z.9781836649373_Code/tf2.0/fake_util.py
76 B
z.9781836649373_Code/tf2.0/moore.csv
2.2 kB
z.9781836649373_Code/tf2.0/plot_rl_rewards.py
548 B
z.9781836649373_Code/tf2.0/rl_trader.py
11 kB
z.9781836649373_Code/tf2.0/sbux.csv
60 kB
z.9781836649373_Code/tf2.0/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/tf2.0/xor3d.py
626 B
z.9781836649373_Code/timeseries/extra_reading.txt
1.1 kB
z.9781836649373_Code/timeseries/WHERE ARE THE NOTEBOOKS.txt
299 B
z.9781836649373_Code/transformers/extra_reading.txt
1.1 kB
z.9781836649373_Code/transformers/WHERE ARE THE NOTEBOOKS.txt
299 B
Chapter 1 Welcome/003. Special Offer.en.srt
1.7 kB
z.9781836649373_Code/unsupervised_class/books.py
7.8 kB
z.9781836649373_Code/unsupervised_class/choose_k.py
807 B
z.9781836649373_Code/unsupervised_class/evolution.py
2.6 kB
z.9781836649373_Code/unsupervised_class/gmm.py
3.1 kB
z.9781836649373_Code/unsupervised_class/gmm_mnist.py
1.2 kB
z.9781836649373_Code/unsupervised_class/hcluster.py
1.7 kB
z.9781836649373_Code/unsupervised_class/kmeans.py
3.5 kB
z.9781836649373_Code/unsupervised_class/kmeans_fail.py
1.6 kB
z.9781836649373_Code/unsupervised_class/kmeans_mnist.py
4.4 kB
z.9781836649373_Code/unsupervised_class/kmeans_visualize.py
2.5 kB
z.9781836649373_Code/unsupervised_class/neural_kmeans.py
877 B
z.9781836649373_Code/unsupervised_class/tweets.py
5.1 kB
Chapter 1 Welcome/003. Special Offer.mp4
3.2 MB
z.9781836649373_Code/unsupervised_class2/autoencoder.py
8.4 kB
z.9781836649373_Code/unsupervised_class2/autoencoder_tf.py
7.7 kB
z.9781836649373_Code/unsupervised_class2/compare_pca_svd.py
751 B
z.9781836649373_Code/unsupervised_class2/extra_reading.txt
1.2 kB
z.9781836649373_Code/unsupervised_class2/gaussian_nb.py
1.8 kB
z.9781836649373_Code/unsupervised_class2/pca.py
1007 B
z.9781836649373_Code/unsupervised_class2/pca_impl.py
1.0 kB
z.9781836649373_Code/unsupervised_class2/rbm.py
4.4 kB
z.9781836649373_Code/unsupervised_class2/rbm_tf.py
4.8 kB
z.9781836649373_Code/unsupervised_class2/sk_mlp.py
829 B
z.9781836649373_Code/unsupervised_class2/tsne_books.py
2.9 kB
z.9781836649373_Code/unsupervised_class2/tsne_donut.py
1.2 kB
z.9781836649373_Code/unsupervised_class2/tsne_mnist.py
1.3 kB
z.9781836649373_Code/unsupervised_class2/tsne_visualization.py
1.4 kB
z.9781836649373_Code/unsupervised_class2/tsne_xor.py
971 B
z.9781836649373_Code/unsupervised_class2/umap_transformer.py
1.2 kB
z.9781836649373_Code/unsupervised_class2/unsupervised.py
4.8 kB
z.9781836649373_Code/unsupervised_class2/util.py
1.0 kB
z.9781836649373_Code/unsupervised_class2/vanishing.py
3.8 kB
z.9781836649373_Code/unsupervised_class2/visualize_features.py
1.9 kB
z.9781836649373_Code/unsupervised_class2/xwing.py
3.8 kB
z.9781836649373_Code/unsupervised_class3/autoencoder_tf.py
2.6 kB
z.9781836649373_Code/unsupervised_class3/autoencoder_theano.py
2.9 kB
z.9781836649373_Code/unsupervised_class3/bayes_classifier_gaussian.py
1.8 kB
z.9781836649373_Code/unsupervised_class3/bayes_classifier_gmm.py
2.2 kB
z.9781836649373_Code/unsupervised_class3/dcgan_tf.py
16 kB
z.9781836649373_Code/unsupervised_class3/dcgan_theano.py
18 kB
z.9781836649373_Code/unsupervised_class3/extra_reading.txt
508 B
z.9781836649373_Code/unsupervised_class3/parameterize_guassian.py
1.3 kB
z.9781836649373_Code/unsupervised_class3/test_stochastic_tensor.py
1.0 kB
z.9781836649373_Code/unsupervised_class3/util.py
4.9 kB
z.9781836649373_Code/unsupervised_class3/vae_tf.py
8.7 kB
z.9781836649373_Code/unsupervised_class3/vae_theano.py
7.6 kB
z.9781836649373_Code/unsupervised_class3/visualize_latent_space.py
1.9 kB