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[] Coursera - Bayesian Methods for Machine Learning

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文件列表

  1. 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 93MB
  2. 006.Variational inference/028. Mean field approximation.mp4 77MB
  3. 007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 76MB
  4. 011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4 70MB
  5. 006.Variational inference/029. Example Ising model.mp4 68MB
  6. 004.Expectation Maximization algorithm/017. E-step details.mp4 66MB
  7. 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 65MB
  8. 005.Applications and examples/022. General EM for GMM.mp4 63MB
  9. 008.MCMC/041. Gibbs sampling.mp4 61MB
  10. 001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 60MB
  11. 007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 59MB
  12. 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 56MB
  13. 001.Introduction to Bayesian methods/005. Linear regression.mp4 50MB
  14. 009.Variational autoencoders/052. Scaling variational EM.mp4 48MB
  15. 008.MCMC/040. Markov Chains.mp4 47MB
  16. 008.MCMC/039. Sampling from 1-d distributions.mp4 47MB
  17. 008.MCMC/047. MCMC for LDA.mp4 47MB
  18. 008.MCMC/038. Monte Carlo estimation.mp4 45MB
  19. 008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 42MB
  20. 005.Applications and examples/025. Probabilistic PCA.mp4 39MB
  21. 011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4 37MB
  22. 003.Latent Variable Models/010. Latent Variable Models.mp4 37MB
  23. 008.MCMC/045. Example of Metropolis-Hastings.mp4 37MB
  24. 010.Variational Dropout/057. Dropout as Bayesian procedure.mp4 35MB
  25. 008.MCMC/048. Bayesian Neural Networks.mp4 34MB
  26. 009.Variational autoencoders/050. Modeling a distribution of images.mp4 32MB
  27. 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 32MB
  28. 003.Latent Variable Models/013. Training GMM.mp4 32MB
  29. 003.Latent Variable Models/014. Example of GMM training.mp4 31MB
  30. 011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4 31MB
  31. 005.Applications and examples/024. K-means, M-step.mp4 31MB
  32. 010.Variational Dropout/056. Learning with priors.mp4 30MB
  33. 008.MCMC/043. Metropolis-Hastings.mp4 30MB
  34. 010.Variational Dropout/058. Sparse variational dropout.mp4 30MB
  35. 003.Latent Variable Models/012. Gaussian Mixture Model.mp4 29MB
  36. 005.Applications and examples/023. K-means from probabilistic perspective.mp4 28MB
  37. 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 28MB
  38. 008.MCMC/042. Example of Gibbs sampling.mp4 28MB
  39. 008.MCMC/046. Markov Chain Monte Carlo summary.mp4 27MB
  40. 009.Variational autoencoders/055. Reparameterization trick.mp4 25MB
  41. 009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4 25MB
  42. 011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4 24MB
  43. 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 24MB
  44. 005.Applications and examples/026. EM for Probabilistic PCA.mp4 22MB
  45. 003.Latent Variable Models/011. Probabilistic clustering.mp4 22MB
  46. 009.Variational autoencoders/054. Log derivative trick.mp4 21MB
  47. 007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 20MB
  48. 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 20MB
  49. 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 19MB
  50. 009.Variational autoencoders/053. Gradient of decoder.mp4 19MB
  51. 004.Expectation Maximization algorithm/018. M-step details.mp4 19MB
  52. 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4 18MB
  53. 011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4 18MB
  54. 006.Variational inference/030. Variational EM & Review.mp4 17MB
  55. 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4 17MB
  56. 007.Latent Dirichlet Allocation/031. Topic modeling.mp4 17MB
  57. 011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4 17MB
  58. 002.Conjugate priors/008. Example Normal, precision.mp4 16MB
  59. 011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4 16MB
  60. 007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4 16MB
  61. 006.Variational inference/027. Why approximate inference.mp4 16MB
  62. 002.Conjugate priors/009. Example Bernoulli.mp4 14MB
  63. 002.Conjugate priors/006. Analytical inference.mp4 14MB
  64. 001.Introduction to Bayesian methods/003. How to define a model.mp4 10MB
  65. 002.Conjugate priors/007. Conjugate distributions.mp4 9MB
  66. 008.MCMC/047. MCMC for LDA.srt 21KB
  67. 009.Variational autoencoders/052. Scaling variational EM.srt 19KB
  68. 008.MCMC/038. Monte Carlo estimation.srt 17KB
  69. 006.Variational inference/029. Example Ising model.srt 17KB
  70. 008.MCMC/039. Sampling from 1-d distributions.srt 16KB
  71. 005.Applications and examples/025. Probabilistic PCA.srt 16KB
  72. 008.MCMC/040. Markov Chains.srt 16KB
  73. 003.Latent Variable Models/010. Latent Variable Models.srt 15KB
  74. 008.MCMC/048. Bayesian Neural Networks.srt 15KB
  75. 005.Applications and examples/022. General EM for GMM.srt 14KB
  76. 009.Variational autoencoders/050. Modeling a distribution of images.srt 14KB
  77. 011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt 14KB
  78. 003.Latent Variable Models/013. Training GMM.srt 14KB
  79. 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt 13KB
  80. 003.Latent Variable Models/014. Example of GMM training.srt 13KB
  81. 004.Expectation Maximization algorithm/017. E-step details.srt 13KB
  82. 003.Latent Variable Models/012. Gaussian Mixture Model.srt 13KB
  83. 008.MCMC/041. Gibbs sampling.srt 13KB
  84. 001.Introduction to Bayesian methods/004. Example thief & alarm.srt 13KB
  85. 011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt 13KB
  86. 008.MCMC/045. Example of Metropolis-Hastings.srt 12KB
  87. 008.MCMC/046. Markov Chain Monte Carlo summary.srt 12KB
  88. 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt 12KB
  89. 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt 12KB
  90. 006.Variational inference/028. Mean field approximation.srt 12KB
  91. 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt 12KB
  92. 001.Introduction to Bayesian methods/005. Linear regression.srt 11KB
  93. 005.Applications and examples/023. K-means from probabilistic perspective.srt 11KB
  94. 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt 11KB
  95. 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt 10KB
  96. 008.MCMC/043. Metropolis-Hastings.srt 10KB
  97. 009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt 10KB
  98. 011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt 10KB
  99. 011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt 9KB
  100. 007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt 9KB
  101. 009.Variational autoencoders/055. Reparameterization trick.srt 9KB
  102. 008.MCMC/042. Example of Gibbs sampling.srt 9KB
  103. 008.MCMC/044. Metropolis-Hastings choosing the critic.srt 9KB
  104. 010.Variational Dropout/056. Learning with priors.srt 9KB
  105. 005.Applications and examples/026. EM for Probabilistic PCA.srt 9KB
  106. 010.Variational Dropout/057. Dropout as Bayesian procedure.srt 8KB
  107. 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt 8KB
  108. 007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt 8KB
  109. 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt 8KB
  110. 003.Latent Variable Models/011. Probabilistic clustering.srt 8KB
  111. 004.Expectation Maximization algorithm/018. M-step details.srt 8KB
  112. 009.Variational autoencoders/054. Log derivative trick.srt 8KB
  113. 009.Variational autoencoders/053. Gradient of decoder.srt 8KB
  114. 006.Variational inference/030. Variational EM & Review.srt 8KB
  115. 010.Variational Dropout/058. Sparse variational dropout.srt 7KB
  116. 011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt 7KB
  117. 007.Latent Dirichlet Allocation/035. LDA E-step, z.srt 7KB
  118. 005.Applications and examples/024. K-means, M-step.srt 7KB
  119. 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt 7KB
  120. 002.Conjugate priors/008. Example Normal, precision.srt 7KB
  121. 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt 7KB
  122. 007.Latent Dirichlet Allocation/031. Topic modeling.srt 7KB
  123. 011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt 6KB
  124. 006.Variational inference/027. Why approximate inference.srt 6KB
  125. 007.Latent Dirichlet Allocation/037. Extensions of LDA.srt 6KB
  126. 011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt 6KB
  127. 002.Conjugate priors/009. Example Bernoulli.srt 5KB
  128. 002.Conjugate priors/006. Analytical inference.srt 5KB
  129. 001.Introduction to Bayesian methods/003. How to define a model.srt 4KB
  130. 002.Conjugate priors/007. Conjugate distributions.srt 3KB
  131. [FTU Forum].url 252B
  132. [FreeCoursesOnline.Me].url 133B
  133. [FreeTutorials.Us].url 119B