Cluster Analysis and Unsupervised Machine Learning in Python

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Last Access Time	Nov. 20, 2024, 8:41 a.m.
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Chapter 7 Setting Up Your Environment (Appendix)/002. Anaconda Environment Setup.mp4 66.4 MB
Chapter 6 Gaussian Mixture Models (GMMs)/002. Write a Gaussian Mixture Model in Python Code.mp4 62.7 MB
Chapter 5 Hierarchical Clustering/005. Application Donald Trump vs. Hillary Clinton Tweets.mp4 50.4 MB
Chapter 7 Setting Up Your Environment (Appendix)/003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.mp4 49.1 MB
Chapter 4 K-Means Clustering/007. Hard K-Means Exercise 3 Solution.mp4 45.6 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 41.6 MB
Chapter 5 Hierarchical Clustering/004. Application Evolution.mp4 37.7 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/004. What order should I take your courses in (part 2).mp4 37.5 MB
Chapter 4 K-Means Clustering/021. K-Means Application Finding Clusters of Related Words.mp4 35.4 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/003. Proof that using Jupyter Notebook is the same as not using it.mp4 34.5 MB
Chapter 6 Gaussian Mixture Models (GMMs)/001. Gaussian Mixture Model (GMM) Algorithm.mp4 30.4 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/001. How to Code Yourself (part 1).mp4 29.6 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/004. How to use Github & Extra Coding Tips (Optional).mp4 29.2 MB
Chapter 4 K-Means Clustering/003. Hard K-Means Exercise 1 Solution.mp4 29.2 MB
Chapter 4 K-Means Clustering/013. Soft K-Means in Python Code.mp4 29.0 MB
Chapter 4 K-Means Clustering/008. Hard K-Means Objective Theory.mp4 28.5 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/003. What order should I take your courses in (part 1).mp4 28.1 MB
Chapter 4 K-Means Clustering/002. Hard K-Means Exercise Prompt 1.mp4 24.7 MB
Chapter 6 Gaussian Mixture Models (GMMs)/008. Expectation-Maximization (pt 1).mp4 23.4 MB
Chapter 6 Gaussian Mixture Models (GMMs)/007. GMM vs Bayes Classifier (pt 2).mp4 22.1 MB
Chapter 3 Unsupervised Learning/002. Why Use Clustering.mp4 21.3 MB
Chapter 4 K-Means Clustering/016. Examples of where K-Means can fail.mp4 20.1 MB
Chapter 6 Gaussian Mixture Models (GMMs)/006. GMM vs Bayes Classifier (pt 1).mp4 19.6 MB
Chapter 4 K-Means Clustering/022. Clustering for NLP and Computer Vision Real-World Applications.mp4 19.5 MB
Chapter 6 Gaussian Mixture Models (GMMs)/003. Practical Issues with GMM.mp4 19.2 MB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/002. How to Code Yourself (part 2).mp4 19.1 MB
Chapter 4 K-Means Clustering/006. Hard K-Means Exercise Prompt 3.mp4 19.1 MB
Chapter 4 K-Means Clustering/001. An Easy Introduction to K-Means Clustering.mp4 17.6 MB
Chapter 4 K-Means Clustering/019. Using K-Means on Real Data MNIST.mp4 17.4 MB
Chapter 4 K-Means Clustering/005. Hard K-Means Exercise 2 Solution.mp4 17.3 MB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/001. How to Succeed in this Course (Long Version).mp4 17.3 MB
Chapter 6 Gaussian Mixture Models (GMMs)/010. Expectation-Maximization (pt 3).mp4 15.4 MB
z.9781836649373_Code/nlp_class/electronics/unlabeled.review 13.9 MB
Chapter 4 K-Means Clustering/009. Hard K-Means Objective Code.mp4 13.8 MB
Chapter 1 Welcome/001. Introduction.mp4 13.6 MB
Chapter 6 Gaussian Mixture Models (GMMs)/005. Kernel Density Estimation.mp4 13.6 MB
Chapter 4 K-Means Clustering/018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 12.8 MB
Chapter 3 Unsupervised Learning/001. What is unsupervised learning used for.mp4 12.5 MB
Chapter 4 K-Means Clustering/004. Hard K-Means Exercise Prompt 2.mp4 11.6 MB
Chapter 4 K-Means Clustering/023. Suggestion Box.mp4 11.1 MB
Chapter 7 Setting Up Your Environment (Appendix)/001. Pre-Installation Check.mp4 11.0 MB
Chapter 5 Hierarchical Clustering/003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 11.0 MB
Chapter 4 K-Means Clustering/020. One Way to Choose K.mp4 10.4 MB
Chapter 4 K-Means Clustering/011. Soft K-Means.mp4 10.1 MB
Chapter 2 Getting Set Up/001. Where to get the code.mp4 9.9 MB
Chapter 1 Welcome/002. Course Outline.mp4 9.5 MB
z.9781836649373_Code/tensorflow/MNIST_data/train-images-idx3-ubyte.gz 9.5 MB
Chapter 6 Gaussian Mixture Models (GMMs)/004. Comparison between GMM and K-Means.mp4 9.0 MB
Chapter 4 K-Means Clustering/015. Visualizing Each Step of K-Means.mp4 7.8 MB
Chapter 4 K-Means Clustering/014. How to Pace Yourself.mp4 7.8 MB
Chapter 5 Hierarchical Clustering/002. Agglomerative Clustering Options.mp4 5.5 MB
Chapter 6 Gaussian Mixture Models (GMMs)/009. Expectation-Maximization (pt 2).mp4 5.0 MB
Chapter 4 K-Means Clustering/010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).mp4 4.1 MB
Chapter 5 Hierarchical Clustering/001. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4 3.8 MB
Chapter 4 K-Means Clustering/017. Disadvantages of K-Means Clustering.mp4 3.7 MB
Chapter 1 Welcome/003. Special Offer.mp4 3.2 MB
Chapter 4 K-Means Clustering/012. The K-Means Objective Function.mp4 3.1 MB
z.9781836649373_Code/bayesian_ml/3/X_set3.csv 2.3 MB
z.9781836649373_Code/tensorflow/MNIST_data/t10k-images-idx3-ubyte.gz 1.6 MB
z.9781836649373_Code/bayesian_ml/1/Xtrain.csv 1.4 MB
z.9781836649373_Code/bayesian_ml/2/Xtrain.csv 1.4 MB
z.9781836649373_Code/rnn_class/gru_nonorm_part1_word_embeddings.npy 1.2 MB
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/mnist_csv/Xtrain.txt 806.4 KB
z.9781836649373_Code/nlp_class/spambase.data 686.5 KB
z.9781836649373_Code/openai/fight.mp4 602.2 KB
z.9781836649373_Code/bayesian_ml/3/X_set2.csv 595.8 KB
z.9781836649373_Code/cnn_class/lena.png 462.7 KB
z.9781836649373_Code/hmm_class/site_data.csv 410.0 KB
z.9781836649373_Code/nlp_class2/ner.txt 348.2 KB
z.9781836649373_Code/bayesian_ml/1/Xtest.csv 235.3 KB
z.9781836649373_Code/bayesian_ml/2/Xtest.csv 235.3 KB
z.9781836649373_Code/cnn_class2/styles/monalisa.jpg 226.0 KB
z.9781836649373_Code/cnn_class2/styles/lesdemoisellesdavignon.jpg 177.0 KB
z.9781836649373_Code/mnist_csv/Q.txt 157.5 KB
z.9781836649373_Code/nlp_class2/w2v_model.npz 156.7 KB
z.9781836649373_Code/openai/robots_playing_soccer.jpeg 137.1 KB
z.9781836649373_Code/nlp_class/all_book_titles.txt 125.0 KB
z.9781836649373_Code/bayesian_ml/1/Q.csv 119.1 KB
z.9781836649373_Code/bayesian_ml/2/Q.csv 119.1 KB
z.9781836649373_Code/bayesian_ml/3/X_set1.csv 95.0 KB
z.9781836649373_Code/cnn_class2/styles/flowercarrier.jpg 93.2 KB
z.9781836649373_Code/mnist_csv/Xtest.txt 80.6 KB
z.9781836649373_Code/cnn_class2/content/sydney.jpg 79.3 KB
z.9781836649373_Code/openai/finance.png 73.3 KB
z.9781836649373_Code/tf2.0/daily-minimum-temperatures-in-me.csv 66.5 KB
z.9781836649373_Code/tf2.0/sbux.csv 60.4 KB
z.9781836649373_Code/hmm_class/robert_frost.txt 55.0 KB
z.9781836649373_Code/openai/handwriting.jpg 52.1 KB
z.9781836649373_Code/openai/webdesign.jpg 49.3 KB
z.9781836649373_Code/cnn_class/helloworld.wav 36.1 KB
z.9781836649373_Code/hmm_class/helloworld.wav 36.1 KB
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 33.8 KB
z.9781836649373_Code/cnn_class2/styles/starrynight.jpg 33.5 KB
z.9781836649373_Code/nlp_class2/w2v_word2idx.json 30.8 KB
z.9781836649373_Code/rnn_class/gru_nonorm_part1_wikipedia_word2idx.json 30.3 KB
z.9781836649373_Code/tf2.0/auto-mpg.data 29.6 KB
z.9781836649373_Code/tensorflow/MNIST_data/train-labels-idx1-ubyte.gz 28.2 KB
Chapter 6 Gaussian Mixture Models (GMMs)/002. Write a Gaussian Mixture Model in Python Code.en.srt 26.4 KB
z.9781836649373_Code/openai/replies.json 26.1 KB
z.9781836649373_Code/hmm_class/edgar_allan_poe.txt 26.0 KB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/004. What order should I take your courses in (part 2).en.srt 25.4 KB
z.9781836649373_Code/openai/physics_problem.jpeg 24.8 KB
z.9781836649373_Code/pytorch/aapl_msi_sbux.csv 23.5 KB
z.9781836649373_Code/tf2.0/aapl_msi_sbux.csv 23.5 KB
z.9781836649373_Code/bayesian_ml/1/ytrain.csv 23.0 KB
z.9781836649373_Code/bayesian_ml/2/ytrain.csv 23.0 KB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/001. How to Code Yourself (part 1).en.srt 22.9 KB
Chapter 4 K-Means Clustering/007. Hard K-Means Exercise 3 Solution.en.srt 22.1 KB
Chapter 7 Setting Up Your Environment (Appendix)/002. Anaconda Environment Setup.en.srt 21.8 KB
Chapter 6 Gaussian Mixture Models (GMMs)/001. Gaussian Mixture Model (GMM) Algorithm.en.srt 21.7 KB
z.9781836649373_Code/cnn_class2/content/elephant.jpg 21.6 KB
Chapter 5 Hierarchical Clustering/005. Application Donald Trump vs. Hillary Clinton Tweets.en.srt 20.5 KB
z.9781836649373_Code/data_csv/X.txt 19.6 KB
z.9781836649373_Code/unsupervised_class3/dcgan_theano.py 18.1 KB
Chapter 5 Hierarchical Clustering/004. Application Evolution.en.srt 18.0 KB
Chapter 4 K-Means Clustering/008. Hard K-Means Objective Theory.en.srt 18.0 KB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/003. What order should I take your courses in (part 1).en.srt 18.0 KB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/004. How to use Github & Extra Coding Tips (Optional).en.srt 16.6 KB
z.9781836649373_Code/unsupervised_class3/dcgan_tf.py 16.0 KB
Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/001. How to Succeed in this Course (Long Version).en.srt 16.0 KB
Chapter 6 Gaussian Mixture Models (GMMs)/007. GMM vs Bayes Classifier (pt 2).en.srt 15.9 KB
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 15.9 KB
Chapter 7 Setting Up Your Environment (Appendix)/003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.en.srt 15.9 KB
Chapter 6 Gaussian Mixture Models (GMMs)/008. Expectation-Maximization (pt 1).en.srt 15.9 KB
Chapter 4 K-Means Clustering/003. Hard K-Means Exercise 1 Solution.en.srt 14.8 KB
z.9781836649373_Code/rl2/atari/dqn_theano.py 14.1 KB
Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/002. How to Code Yourself (part 2).en.srt 13.9 KB
z.9781836649373_Code/kerascv/imagenet_label_names.json 13.9 KB
Chapter 6 Gaussian Mixture Models (GMMs)/003. Practical Issues with GMM.en.srt 13.6 KB
Chapter 6 Gaussian Mixture Models (GMMs)/006. GMM vs Bayes Classifier (pt 1).en.srt 13.3 KB
z.9781836649373_Code/rl2/atari/dqn_tf.py 13.3 KB
z.9781836649373_Code/nlp_class3/attention.py 13.1 KB
z.9781836649373_Code/nlp_class2/word2vec_tf.py 13.0 KB
Chapter 3 Unsupervised Learning/002. Why Use Clustering.en.srt 12.9 KB
z.9781836649373_Code/rl/tic_tac_toe.py 12.6 KB
Chapter 4 K-Means Clustering/002. Hard K-Means Exercise Prompt 1.en.srt 12.4 KB
z.9781836649373_Code/nlp_class3/memory_network.py 12.2 KB
z.9781836649373_Code/ann_logistic_extra/ecommerce_data.csv 12.1 KB
z.9781836649373_Code/nlp_class2/glove.py 11.7 KB
z.9781836649373_Code/nlp_class2/word2vec_theano.py 11.6 KB
z.9781836649373_Code/pytorch/rl_trader.py 11.6 KB
z.9781836649373_Code/cnn_class2/siamese.py 11.6 KB
z.9781836649373_Code/nlp_class2/rntn_theano.py 11.4 KB
Chapter 6 Gaussian Mixture Models (GMMs)/010. Expectation-Maximization (pt 3).en.srt 10.8 KB
z.9781836649373_Code/nlp_class2/rntn_tensorflow_rnn.py 10.7 KB
z.9781836649373_Code/rl3/ddpg.py 10.7 KB
z.9781836649373_Code/tf2.0/rl_trader.py 10.7 KB
z.9781836649373_Code/nlp_class3/wseq2seq.py 10.5 KB
z.9781836649373_Code/supervised_class/dt_without_recursion.py 10.4 KB
z.9781836649373_Code/nlp_class2/word2vec.py 10.3 KB
Chapter 4 K-Means Clustering/001. An Easy Introduction to K-Means Clustering.en.srt 10.0 KB
z.9781836649373_Code/hmm_class/hmmc_scaled_concat_diag.py 10.0 KB
z.9781836649373_Code/rl3/a2c/atari_wrappers.py 9.9 KB
z.9781836649373_Code/mnist_csv/label_train.txt 9.8 KB
z.9781836649373_Code/hmm_class/hmmc.py 9.7 KB
z.9781836649373_Code/nlp_class2/recursive_theano.py 9.7 KB
z.9781836649373_Code/rl/linear_rl_trader.py 9.6 KB
z.9781836649373_Code/rl/grid_world.py 9.5 KB
Chapter 4 K-Means Clustering/006. Hard K-Means Exercise Prompt 3.en.srt 9.5 KB
Chapter 4 K-Means Clustering/013. Soft K-Means in Python Code.en.srt 9.5 KB
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/018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).en.srt 9.4 KB
z.9781836649373_Code/svm_class/svm_smo.py 9.3 KB
Chapter 4 K-Means Clustering/021. K-Means Application Finding Clusters of Related Words.en.srt 9.1 KB
Chapter 6 Gaussian Mixture Models (GMMs)/005. Kernel Density Estimation.en.srt 9.1 KB
z.9781836649373_Code/data_csv/X_orig.txt 9.0 KB
z.9781836649373_Code/nlp_class2/pmi.py 9.0 KB
Chapter 4 K-Means Clustering/005. Hard K-Means Exercise 2 Solution.en.srt 9.0 KB
z.9781836649373_Code/rnn_class/util.py 8.8 KB
z.9781836649373_Code/hmm_class/hmmc_scaled_concat.py 8.8 KB
z.9781836649373_Code/rl2/a3c/worker.py 8.8 KB
z.9781836649373_Code/unsupervised_class3/vae_tf.py 8.7 KB
z.9781836649373_Code/hmm_class/hmmc_concat.py 8.6 KB
z.9781836649373_Code/rl3/a2c/a2c.py 8.5 KB
z.9781836649373_Code/nlp_class2/glove_theano.py 8.4 KB
z.9781836649373_Code/unsupervised_class2/autoencoder.py 8.4 KB
z.9781836649373_Code/recommenders/rbm_tf_k.py 8.3 KB
z.9781836649373_Code/nlp_class2/glove_tf.py 8.0 KB
z.9781836649373_Code/ann_class2/util.py 7.9 KB
z.9781836649373_Code/ab_testing/advertisement_clicks.csv 7.8 KB
z.9781836649373_Code/unsupervised_class/books.py 7.8 KB
z.9781836649373_Code/nlp_class2/rntn_tensorflow.py 7.8 KB
Chapter 3 Unsupervised Learning/001. What is unsupervised learning used for.en.srt 7.7 KB
z.9781836649373_Code/unsupervised_class2/autoencoder_tf.py 7.7 KB
z.9781836649373_Code/rl2/cartpole/pg_tf.py 7.7 KB
z.9781836649373_Code/hmm_class/hmmc_tf.py 7.7 KB
z.9781836649373_Code/rnn_class/srn_language_tf.py 7.6 KB
Chapter 4 K-Means Clustering/011. Soft K-Means.en.srt 7.6 KB
z.9781836649373_Code/unsupervised_class3/vae_theano.py 7.6 KB
z.9781836649373_Code/rnn_class/srn_language.py 7.5 KB
z.9781836649373_Code/cnn_class2/tf_resnet.py 7.5 KB
Chapter 1 Welcome/001. Introduction.en.srt 7.5 KB
Chapter 4 K-Means Clustering/019. Using K-Means on Real Data MNIST.en.srt 7.2 KB
z.9781836649373_Code/recommenders/rbm_tf_k_faster.py 7.2 KB
z.9781836649373_Code/rl2/mountaincar/pg_tf_random.py 7.2 KB
z.9781836649373_Code/rl2/cartpole/pg_theano.py 7.1 KB
z.9781836649373_Code/hmm_class/hmmd.py 7.1 KB
z.9781836649373_Code/cnn_class/cnn_tf.py 7.1 KB
z.9781836649373_Code/nlp_class2/recursive_tensorflow.py 7.0 KB
z.9781836649373_Code/rl2/mountaincar/pg_theano.py 7.0 KB
z.9781836649373_Code/cnn_class/cifar.py 7.0 KB
Chapter 7 Setting Up Your Environment (Appendix)/001. Pre-Installation Check.en.srt 6.9 KB
z.9781836649373_Code/nlp_class2/pos_baseline.py 6.9 KB
z.9781836649373_Code/hmm_class/hmmc_theano2.py 6.9 KB
z.9781836649373_Code/rl2/cartpole/dqn_tf.py 6.8 KB
z.9781836649373_Code/rnn_class/batch_wiki.py 6.8 KB
z.9781836649373_Code/rl2/cartpole/dqn_theano.py 6.8 KB
z.9781836649373_Code/README.md 6.8 KB
z.9781836649373_Code/cnn_class/cnn_theano_plot_filters.py 6.8 KB
z.9781836649373_Code/supervised_class/dt.py 6.7 KB
z.9781836649373_Code/rnn_class/rrnn_language.py 6.7 KB
Chapter 4 K-Means Clustering/016. Examples of where K-Means can fail.en.srt 6.7 KB
z.9781836649373_Code/rnn_class/batch_units.py 6.7 KB
z.9781836649373_Code/cnn_class/cnn_tf_plot_filters.py 6.7 KB
z.9781836649373_Code/nlp_class2/glove_svd.py 6.6 KB
z.9781836649373_Code/nlp_class2/pos_tf.py 6.6 KB
z.9781836649373_Code/data_csv/legend.txt 6.6 KB
Chapter 1 Welcome/002. Course Outline.en.srt 6.5 KB
z.9781836649373_Code/rl2/mountaincar/pg_tf.py 6.5 KB
z.9781836649373_Code/rnn_class/wiki.py 6.5 KB
Chapter 4 K-Means Clustering/004. Hard K-Means Exercise Prompt 2.en.srt 6.5 KB
Chapter 4 K-Means Clustering/009. Hard K-Means Objective Code.en.srt 6.5 KB
z.9781836649373_Code/rl2/mountaincar/q_learning.py 6.4 KB
z.9781836649373_Code/nlp_class2/util.py 6.4 KB
z.9781836649373_Code/bayesian_ml/4/npbgmm.py 6.4 KB
Chapter 2 Getting Set Up/001. Where to get the code.en.srt 6.3 KB
z.9781836649373_Code/hmm_class/hmmc_theano.py 6.3 KB
z.9781836649373_Code/bayesian_ml/4/vigmm.py 6.2 KB
z.9781836649373_Code/ann_class2/momentum.py 6.2 KB
z.9781836649373_Code/hmm_class/hmmd_scaled.py 6.1 KB
z.9781836649373_Code/cnn_class2/tf_resnet_convblock.py 6.1 KB
z.9781836649373_Code/rl2/mountaincar/pg_theano_random.py 6.0 KB
z.9781836649373_Code/nlp_class3/poetry.py 6.0 KB
z.9781836649373_Code/rl3/es_flappy.py 6.0 KB
z.9781836649373_Code/ann_class2/adam.py 5.9 KB
z.9781836649373_Code/svm_class/kernel_svm_gradient_primal.py 5.8 KB
z.9781836649373_Code/nlp_class2/ner_tf.py 5.8 KB
z.9781836649373_Code/ann_class2/batch_norm_theano.py 5.8 KB
z.9781836649373_Code/tensorflow/input_data.py 5.7 KB
z.9781836649373_Code/rnn_class/batch_parity.py 5.7 KB
z.9781836649373_Code/linear_regression_class/moore.csv 5.7 KB
Chapter 4 K-Means Clustering/020. One Way to Choose K.en.srt 5.7 KB
z.9781836649373_Code/nlp_class/sentiment.py 5.6 KB
Chapter 5 Hierarchical Clustering/002. Agglomerative Clustering Options.en.srt 5.6 KB
z.9781836649373_Code/ann_class2/batch_norm_tf.py 5.6 KB
z.9781836649373_Code/nlp_class2/pos_ner_keras.py 5.6 KB
z.9781836649373_Code/cnn_class/cnn_theano.py 5.6 KB
z.9781836649373_Code/airline/ann.py 5.4 KB
Chapter 6 Gaussian Mixture Models (GMMs)/004. Comparison between GMM and K-Means.en.srt 5.4 KB
z.9781836649373_Code/kerascv/pascal2coco.py 5.4 KB
z.9781836649373_Code/ann_class2/sgd.py 5.3 KB
z.9781836649373_Code/unsupervised_class/tweets.py 5.1 KB
z.9781836649373_Code/nlp_class2/pos_rnn.py 5.0 KB
Chapter 5 Hierarchical Clustering/003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.en.srt 5.0 KB
z.9781836649373_Code/ann_class2/dropout_theano.py 5.0 KB
z.9781836649373_Code/recommenders/userbased.py 5.0 KB
z.9781836649373_Code/recommenders/itembased.py 5.0 KB
z.9781836649373_Code/ann_class2/pytorch_dropout.py 5.0 KB
z.9781836649373_Code/airline/rnn.py 5.0 KB
Chapter 4 K-Means Clustering/014. How to Pace Yourself.en.srt 4.9 KB
z.9781836649373_Code/ann_class2/dropout_tensorflow.py 4.9 KB
z.9781836649373_Code/unsupervised_class3/util.py 4.9 KB
z.9781836649373_Code/ann_class2/pytorch_batchnorm.py 4.9 KB
z.9781836649373_Code/unsupervised_class2/unsupervised.py 4.8 KB
z.9781836649373_Code/unsupervised_class2/rbm_tf.py 4.8 KB
z.9781836649373_Code/rnn_class/poetry_classifier.py 4.8 KB
z.9781836649373_Code/nlp_class2/neural_network2.py 4.7 KB
z.9781836649373_Code/hmm_class/hmmd_theano2.py 4.7 KB
z.9781836649373_Code/cnn_class2/use_pretrained_weights_resnet.py 4.7 KB
z.9781836649373_Code/rl3/es_mujoco.py 4.7 KB
z.9781836649373_Code/cnn_class2/use_pretrained_weights_vgg.py 4.7 KB
z.9781836649373_Code/hmm_class/hmmd_tf.py 4.7 KB
z.9781836649373_Code/cnn_class2/style_transfer1.py 4.7 KB
z.9781836649373_Code/svm_class/svm_gradient.py 4.7 KB
z.9781836649373_Code/rl2/mountaincar/n_step.py 4.7 KB
Chapter 4 K-Means Clustering/023. Suggestion Box.en.srt 4.6 KB
z.9781836649373_Code/rl2/cartpole/q_learning.py 4.6 KB
z.9781836649373_Code/ann_class2/rmsprop.py 4.6 KB
z.9781836649373_Code/cnn_class2/ssd.py 4.5 KB
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z.9781836649373_Code/rl/monte_carlo_es.py 4.1 KB
z.9781836649373_Code/ann_class/backprop.py 4.1 KB
z.9781836649373_Code/rl/policy_iteration_deterministic.py 4.1 KB
z.9781836649373_Code/linear_regression_class/data_2d.csv 4.1 KB
z.9781836649373_Code/nlp_class2/markov.py 4.1 KB
z.9781836649373_Code/nlp_class3/lstm_toxic.py 4.1 KB
z.9781836649373_Code/svm_class/fake_neural_net.py 4.1 KB
z.9781836649373_Code/nlp_class2/pretrained_glove.py 4.0 KB
z.9781836649373_Code/cnn_class2/style_transfer2.py 4.0 KB
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z.9781836649373_Code/nlp_class2/neural_network.py 4.0 KB
z.9781836649373_Code/rl2/cartpole/q_learning_bins.py 4.0 KB
Chapter 4 K-Means Clustering/010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).en.srt 4.0 KB
z.9781836649373_Code/rl/policy_iteration_probabilistic.py 4.0 KB
z.9781836649373_Code/supervised_class2/bias_variance_demo.py 4.0 KB
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z.9781836649373_Code/rl/cartpole.py 3.9 KB
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z.9781836649373_Code/bayesian_ml/2/ytest.csv 3.9 KB
z.9781836649373_Code/nlp_class2/bow_classifier.py 3.9 KB
Chapter 5 Hierarchical Clustering/001. Visual Walkthrough of Agglomerative Hierarchical Clustering.en.srt 3.9 KB
z.9781836649373_Code/unsupervised_class2/vanishing.py 3.8 KB
z.9781836649373_Code/cnn_class2/style_transfer3.py 3.8 KB
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z.9781836649373_Code/unsupervised_class/kmeans.py 3.5 KB
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Chapter 4 K-Means Clustering/017. Disadvantages of K-Means Clustering.en.srt 3.4 KB
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z.9781836649373_Code/nlp_class/lsa.py 3.4 KB
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z.9781836649373_Code/ann_class2/cntk_example.py 3.2 KB
z.9781836649373_Code/supervised_class/knn.py 3.1 KB
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Chapter 6 Gaussian Mixture Models (GMMs)/009. Expectation-Maximization (pt 2).en.srt 2.9 KB
z.9781836649373_Code/unsupervised_class2/tsne_books.py 2.9 KB
Chapter 4 K-Means Clustering/015. Visualizing Each Step of K-Means.en.srt 2.9 KB
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