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Advanced Machine Learning Specialization

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Advanced Machine Learning Specialization

文件列表
  1. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/02_conjugate-distributions.mp45.33MB
  2. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/03_how-to-define-a-model.mp45.85MB
  3. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/01_analytical-inference.mp47.62MB
  4. 2. competitive-data-science/11_ensembling/01_ensembling/01_introduction-into-ensemble-methods.mp48MB
  5. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/04_example-bernoulli.mp48.03MB
  6. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/07_extensions-of-lda.mp49.17MB
  7. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/01_why-approximate-inference.mp49.2MB
  8. 2. competitive-data-science/03_final-project-description/01_final-project/02_final-project-overview.mp49.31MB
  9. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/07_application-of-bayesian-optimization.mp49.55MB
  10. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/03_example-normal-precision.mp49.57MB
  11. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/03_gp-for-machine-learning.mp49.63MB
  12. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/01_topic-modeling.mp49.7MB
  13. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/02_bayesian-approach-to-statistics.mp49.77MB
  14. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/04_variational-em-review.mp410.14MB
  15. 1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/03_gradient-descent.mp410.25MB
  16. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/01_nonparametric-methods.mp410.54MB
  17. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/03_latent-dirichlet-allocation.mp410.56MB
  18. 1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/01_learning-new-tasks-with-pre-trained-cnns.mp410.67MB
  19. 1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/02_model-regularization.mp410.75MB
  20. 2. competitive-data-science/01_introduction-recap/02_competition-mechanics/03_real-world-application-vs-competitions.mp411.02MB
  21. 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/03_feature-interactions.mp411.11MB
  22. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/05_gradient-of-decoder.mp411.34MB
  23. 1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/01_stochastic-gradient-descent.mp411.4MB
  24. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/04_m-step-details.mp411.43MB
  25. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/01_scaling-variational-inference-unbiased-estimates.mp411.49MB
  26. 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/01_statistics-and-distance-based-features.mp411.6MB
  27. 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/04_t-sne.mp411.62MB
  28. 2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/01_software-hardware-requirements.mp411.75MB
  29. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/02_dirichlet-distribution.mp411.88MB
  30. 1. intro-to-deep-learning/04_unsupervised-representation-learning/01_intro-to-unsupervised-learning/02_autoencoders-101.mp411.9MB
  31. 2. competitive-data-science/11_ensembling/01_ensembling/02_bagging.mp411.94MB
  32. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/07_summary-of-expectation-maximization.mp412.1MB
  33. 2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/03_numerai-competition-eda.mp412.21MB
  34. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/06_log-derivative-trick.mp412.24MB
  35. 2. competitive-data-science/06_data-leakages/01_data-leakages/01_basic-data-leaks.mp412.28MB
  36. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/02_probabilistic-clustering.mp412.4MB
  37. 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/03_building-intuition-about-the-data.mp412.7MB
  38. 1. intro-to-deep-learning/04_unsupervised-representation-learning/01_intro-to-unsupervised-learning/01_unsupervised-learning-what-it-is-and-why-bother.mp412.74MB
  39. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/05_em-for-probabilistic-pca.mp412.98MB
  40. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/06_general-approaches-for-metrics-optimization.mp413.12MB
  41. 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/02_matrix-factorizations.mp413.16MB
  42. 2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/01_springleaf-competition-eda-i.mp413.18MB
  43. 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/03_springleaf-marketing-response.mp413.29MB
  44. 1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/01_multilayer-perceptron.mp413.4MB
  45. 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/02_exploratory-data-analysis.mp413.51MB
  46. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/01_think-bayesian-statistics-review.mp413.55MB
  47. 1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/02_gradient-descent-extensions.mp413.63MB
  48. 1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/02_deep-learning-as-a-language.mp413.66MB
  49. 2. competitive-data-science/01_introduction-recap/02_competition-mechanics/01_competition-mechanics.mp413.69MB
  50. 2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/02_hyperparameter-tuning-i.mp413.73MB
  51. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/03_sparse-variational-dropout.mp413.76MB
  52. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/01_learning-with-priors.mp413.81MB
  53. 1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/02_training-a-neural-network.mp413.95MB
  54. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/09_classification-metrics-optimization-ii.mp413.96MB
  55. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/01_overview.mp414.08MB
  56. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/02_gaussian-processes.mp414.14MB
  57. 2. competitive-data-science/05_validation/01_validation/02_validation-strategies.mp414.19MB
  58. 1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/01_overfitting-problem-and-model-validation.mp414.22MB
  59. 1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/01_the-training-of-rnns-is-not-that-easy.mp414.51MB
  60. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/03_using-cnns-with-a-mixture-of-gaussians.mp414.61MB
  61. 1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/01_generative-models-101.mp414.62MB
  62. 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/06_dataset-cleaning-and-other-things-to-check.mp414.7MB
  63. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/08_classification-metrics-optimization-i.mp414.71MB
  64. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/07_reparameterization-trick.mp414.74MB
  65. 1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/03_backpropagation-primer.mp414.96MB
  66. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/03_k-means-m-step.mp415.26MB
  67. 1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/02_autoencoder-applications-image-generation-data-visualization-more.mp415.33MB
  68. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/02_dropout-as-bayesian-procedure.mp415.47MB
  69. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/05_example-of-gibbs-sampling.mp415.54MB
  70. 2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/02_regularization.mp415.63MB
  71. 1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/03_gradients-optimization-in-tensorflow.mp415.65MB
  72. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/02_motivation.mp415.76MB
  73. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/09_markov-chain-monte-carlo-summary.mp415.83MB
  74. 1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/01_motivation-for-recurrent-layers.mp415.99MB
  75. 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/05_walmart-trip-type-classification.mp416.29MB
  76. 1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/01_what-deep-learning-is-and-is-not.mp416.32MB
  77. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/04_regression-metrics-review-ii.mp416.61MB
  78. 1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/02_a-glimpse-of-other-computer-vision-tasks.mp416.86MB
  79. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/06_metropolis-hastings.mp416.86MB
  80. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/01_jensens-inequality-kullback-leibler-divergence.mp416.87MB
  81. 2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/01_concept-of-mean-encoding.mp416.92MB
  82. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/02_k-means-from-probabilistic-perspective.mp416.93MB
  83. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/03_gaussian-mixture-model.mp417.5MB
  84. 2. competitive-data-science/01_introduction-recap/01_welcome-to-how-to-win-a-data-science-competition/02_course-overview.mp417.6MB
  85. 1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/02_overview-of-modern-cnn-architectures.mp417.7MB
  86. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/04_datetime-and-coordinates.mp417.73MB
  87. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/06_bayesian-optimization.mp418.17MB
  88. 2. competitive-data-science/01_introduction-recap/02_competition-mechanics/02_kaggle-overview-screencast.mp418.32MB
  89. 2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/01_recap-of-main-ml-algorithms.mp418.32MB
  90. 1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/02_simple-rnn-and-backpropagation.mp418.36MB
  91. 1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/01_keras-introduction.mp418.51MB
  92. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/05_example-of-gmm-training.mp418.53MB
  93. 1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/02_dealing-with-vanishing-and-exploding-gradients.mp418.65MB
  94. 2. competitive-data-science/05_validation/01_validation/01_validation-and-overfitting.mp418.82MB
  95. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/04_training-gmm.mp418.87MB
  96. 2. competitive-data-science/06_data-leakages/01_data-leakages/02_leaderboard-probing-and-examples-of-rare-data-leaks.mp418.92MB
  97. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/02_modeling-a-distribution-of-images.mp418.98MB
  98. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm.mp419.01MB
  99. 1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/01_going-deeper-with-tensorflow.mp419.14MB
  100. 1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/01_linear-regression.mp419.18MB
  101. 2. competitive-data-science/06_data-leakages/01_data-leakages/03_expedia-challenge.mp419.49MB
  102. 2. competitive-data-science/11_ensembling/01_ensembling/06_ensembling-tips-and-tricks.mp419.55MB
  103. 1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/02_generative-adversarial-networks.mp419.79MB
  104. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/07_regression-metrics-optimization.mp419.95MB
  105. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/11_bayesian-neural-networks.mp420.02MB
  106. 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/02_crowdflower-competition.mp420.14MB
  107. 1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/01_natural-language-processing-primer.mp420.21MB
  108. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/07_metropolis-hastings-choosing-the-critic.mp420.27MB
  109. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/08_example-of-metropolis-hastings.mp420.49MB
  110. 1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/01_autoencoder-applications.mp420.53MB
  111. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/05_handling-missing-values.mp420.93MB
  112. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/05_nuances-of-gp.mp421.26MB
  113. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/01_bag-of-words.mp421.3MB
  114. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/01_latent-variable-models.mp421.31MB
  115. 2. competitive-data-science/11_ensembling/01_ensembling/05_stacknet.mp421.4MB
  116. 2. competitive-data-science/11_ensembling/01_ensembling/03_boosting.mp421.56MB
  117. 2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/03_extensions-and-generalizations.mp421.68MB
  118. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/03_categorical-and-ordinal-features.mp422.28MB
  119. 1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/03_applications-of-adversarial-approach.mp422.79MB
  120. 1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/01_motivation-for-convolutional-layers.mp422.79MB
  121. 1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/02_linear-classification.mp422.82MB
  122. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/04_probabilistic-pca.mp423.12MB
  123. 1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/02_our-first-cnn-architecture.mp423.28MB
  124. 2. competitive-data-science/11_ensembling/01_ensembling/04_stacking.mp423.3MB
  125. 2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/03_hyperparameter-tuning-ii.mp423.79MB
  126. 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/05_visualizations.mp423.89MB
  127. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/05_linear-regression.mp424.35MB
  128. 1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/03_modern-rnns-lstm-and-gru.mp424.55MB
  129. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/01_monte-carlo-estimation.mp425.13MB
  130. 2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/04_hyperparameter-tuning-iii.mp425.68MB
  131. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/05_example-em-for-discrete-mixture-e-step.mp425.69MB
  132. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/02_word2vec-cnn.mp425.84MB
  133. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/05_lda-e-step-z.mp426.09MB
  134. 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/04_exploring-anonymized-data.mp426.31MB
  135. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/03_markov-chains.mp426.45MB
  136. 1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/02_word-embeddings.mp426.45MB
  137. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/03_regression-metrics-review-i.mp426.45MB
  138. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/02_sampling-from-1-d-distributions.mp426.59MB
  139. 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/02_numeric-features.mp426.85MB
  140. 2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/02_springleaf-competition-eda-ii.mp427.56MB
  141. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/10_mcmc-for-lda.mp427.63MB
  142. 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/04_example-thief-alarm.mp427.67MB
  143. 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/04_scaling-variational-em.mp427.69MB
  144. 1. intro-to-deep-learning/05_deep-learning-for-sequences/03_applications-of-rnns/01_practical-use-cases-for-rnns.mp429.11MB
  145. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/06_example-em-for-discrete-mixture-m-step.mp429.3MB
  146. 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/04_gibbs-sampling.mp429.32MB
  147. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/01_general-em-for-gmm.mp429.47MB
  148. 2. competitive-data-science/05_validation/01_validation/04_data-splitting-strategies.mp430.05MB
  149. 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/03_e-step-details.mp430.4MB
  150. 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/04_derivation-of-main-formula.mp431.09MB
  151. 1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/01_training-tips-and-tricks-for-deep-cnns.mp431.33MB
  152. 2. competitive-data-science/09_hyperparameter-optimization/02_tips-and-tricks/01_practical-guide.mp432.82MB
  153. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/04_lda-e-step-theta.mp433.36MB
  154. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/03_example-ising-model.mp433.5MB
  155. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/02_mean-field-approximation.mp435.44MB
  156. 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/04_microsoft-malware-classification-challenge.mp437.84MB
  157. 2. competitive-data-science/05_validation/01_validation/05_problems-occurring-during-validation.mp439.49MB
  158. 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/05_classification-metrics-review.mp439.59MB
  159. 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/06_lda-m-step-prediction.mp440.57MB
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