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

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  3. Creat Time：2024-08-20
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  5. Last Active：2024-11-25
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    [FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning

  1. 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 95.71 MB
  2. 006.Variational inference/028. Mean field approximation.mp4 79.16 MB
  3. 007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 77.38 MB
  4. 011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4 71.54 MB
  5. 006.Variational inference/029. Example Ising model.mp4 69.87 MB
  6. 004.Expectation Maximization algorithm/017. E-step details.mp4 67.83 MB
  7. 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 67.04 MB
  8. 005.Applications and examples/022. General EM for GMM.mp4 64.03 MB
  9. 008.MCMC/041. Gibbs sampling.mp4 62.88 MB
  10. 001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 61.28 MB
  11. 007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 60.65 MB
  12. 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 57.73 MB
  13. 001.Introduction to Bayesian methods/005. Linear regression.mp4 51.27 MB
  14. 009.Variational autoencoders/052. Scaling variational EM.mp4 48.93 MB
  15. 008.MCMC/040. Markov Chains.mp4 48.19 MB
  16. 008.MCMC/039. Sampling from 1-d distributions.mp4 48.18 MB
  17. 008.MCMC/047. MCMC for LDA.mp4 47.80 MB
  18. 008.MCMC/038. Monte Carlo estimation.mp4 45.57 MB
  19. 008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 43.02 MB
  20. 005.Applications and examples/025. Probabilistic PCA.mp4 39.91 MB
  21. 011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4 37.69 MB
  22. 003.Latent Variable Models/010. Latent Variable Models.mp4 37.67 MB
  23. 008.MCMC/045. Example of Metropolis-Hastings.mp4 37.49 MB
  24. 010.Variational Dropout/057. Dropout as Bayesian procedure.mp4 35.87 MB
  25. 008.MCMC/048. Bayesian Neural Networks.mp4 34.85 MB
  26. 009.Variational autoencoders/050. Modeling a distribution of images.mp4 33.02 MB
  27. 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 32.73 MB
  28. 003.Latent Variable Models/013. Training GMM.mp4 32.37 MB
  29. 003.Latent Variable Models/014. Example of GMM training.mp4 32.03 MB
  30. 011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4 31.98 MB
  31. 005.Applications and examples/024. K-means, M-step.mp4 31.69 MB
  32. 010.Variational Dropout/056. Learning with priors.mp4 31.11 MB
  33. 008.MCMC/043. Metropolis-Hastings.mp4 30.62 MB
  34. 010.Variational Dropout/058. Sparse variational dropout.mp4 30.33 MB
  35. 003.Latent Variable Models/012. Gaussian Mixture Model.mp4 29.86 MB
  36. 005.Applications and examples/023. K-means from probabilistic perspective.mp4 29.14 MB
  37. 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 29.04 MB
  38. 008.MCMC/042. Example of Gibbs sampling.mp4 28.25 MB
  39. 008.MCMC/046. Markov Chain Monte Carlo summary.mp4 27.47 MB
  40. 009.Variational autoencoders/055. Reparameterization trick.mp4 25.79 MB
  41. 009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4 25.45 MB
  42. 011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4 24.76 MB
  43. 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 24.26 MB
  44. 005.Applications and examples/026. EM for Probabilistic PCA.mp4 22.32 MB
  45. 003.Latent Variable Models/011. Probabilistic clustering.mp4 22.22 MB
  46. 009.Variational autoencoders/054. Log derivative trick.mp4 21.29 MB
  47. 007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 20.98 MB
  48. 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 20.77 MB
  49. 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 19.96 MB
  50. 009.Variational autoencoders/053. Gradient of decoder.mp4 19.77 MB
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