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[FreeCoursesOnline.Me] [Coursera] Bayesian Methods for Machine Learning - [FCO]

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视频 2020-9-10 12:52 2024-11-16 05:10 221 2.2 GB 65
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FreeCoursesOnline Me Coursera Bayesian Methods for Machine Learning - FCO

文件列表
  1. 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp423.69MB
  2. 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp417.07MB
  3. 001.Introduction to Bayesian methods/003. How to define a model.mp410.05MB
  4. 001.Introduction to Bayesian methods/004. Example thief & alarm.mp459.85MB
  5. 001.Introduction to Bayesian methods/005. Linear regression.mp450.06MB
  6. 002.Conjugate priors/006. Analytical inference.mp413.82MB
  7. 002.Conjugate priors/007. Conjugate distributions.mp49.22MB
  8. 002.Conjugate priors/008. Example Normal, precision.mp416.41MB
  9. 002.Conjugate priors/009. Example Bernoulli.mp414.02MB
  10. 003.Latent Variable Models/010. Latent Variable Models.mp436.78MB
  11. 003.Latent Variable Models/011. Probabilistic clustering.mp421.7MB
  12. 003.Latent Variable Models/012. Gaussian Mixture Model.mp429.16MB
  13. 003.Latent Variable Models/013. Training GMM.mp431.61MB
  14. 003.Latent Variable Models/014. Example of GMM training.mp431.27MB
  15. 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp428.36MB
  16. 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp431.97MB
  17. 004.Expectation Maximization algorithm/017. E-step details.mp466.24MB
  18. 004.Expectation Maximization algorithm/018. M-step details.mp419.21MB
  19. 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp456.37MB
  20. 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp465.47MB
  21. 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp420.29MB
  22. 005.Applications and examples/022. General EM for GMM.mp462.53MB
  23. 005.Applications and examples/023. K-means from probabilistic perspective.mp428.46MB
  24. 005.Applications and examples/024. K-means, M-step.mp430.95MB
  25. 005.Applications and examples/025. Probabilistic PCA.mp438.98MB
  26. 005.Applications and examples/026. EM for Probabilistic PCA.mp421.8MB
  27. 006.Variational inference/027. Why approximate inference.mp415.74MB
  28. 006.Variational inference/028. Mean field approximation.mp477.3MB
  29. 006.Variational inference/029. Example Ising model.mp468.23MB
  30. 006.Variational inference/030. Variational EM & Review.mp417.38MB
  31. 007.Latent Dirichlet Allocation/031. Topic modeling.mp416.76MB
  32. 007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp420.49MB
  33. 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp418.22MB
  34. 007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp475.56MB
  35. 007.Latent Dirichlet Allocation/035. LDA E-step, z.mp459.22MB
  36. 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp493.47MB
  37. 007.Latent Dirichlet Allocation/037. Extensions of LDA.mp415.83MB
  38. 008.MCMC/038. Monte Carlo estimation.mp444.51MB
  39. 008.MCMC/039. Sampling from 1-d distributions.mp447.05MB
  40. 008.MCMC/040. Markov Chains.mp447.06MB
  41. 008.MCMC/041. Gibbs sampling.mp461.41MB
  42. 008.MCMC/042. Example of Gibbs sampling.mp427.59MB
  43. 008.MCMC/043. Metropolis-Hastings.mp429.9MB
  44. 008.MCMC/044. Metropolis-Hastings choosing the critic.mp442.01MB
  45. 008.MCMC/045. Example of Metropolis-Hastings.mp436.61MB
  46. 008.MCMC/046. Markov Chain Monte Carlo summary.mp426.83MB
  47. 008.MCMC/047. MCMC for LDA.mp446.68MB
  48. 008.MCMC/048. Bayesian Neural Networks.mp434.03MB
  49. 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp419.5MB
  50. 009.Variational autoencoders/050. Modeling a distribution of images.mp432.24MB
  51. 009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp424.85MB
  52. 009.Variational autoencoders/052. Scaling variational EM.mp447.78MB
  53. 009.Variational autoencoders/053. Gradient of decoder.mp419.31MB
  54. 009.Variational autoencoders/054. Log derivative trick.mp420.79MB
  55. 009.Variational autoencoders/055. Reparameterization trick.mp425.18MB
  56. 010.Variational Dropout/056. Learning with priors.mp430.39MB
  57. 010.Variational Dropout/057. Dropout as Bayesian procedure.mp435.03MB
  58. 010.Variational Dropout/058. Sparse variational dropout.mp429.61MB
  59. 011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp418.16MB
  60. 011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp424.18MB
  61. 011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp416.36MB
  62. 011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp469.86MB
  63. 011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp436.81MB
  64. 011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp431.23MB
  65. 011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp416.61MB
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