[CourseClub.NET] Coursera - Bayesian Methods for Machine Learning

磁力链接/BT种子名称

[CourseClub.NET] Coursera - Bayesian Methods for Machine Learning

磁力链接/BT种子简介

种子哈希：d9950c61565fb6179880197913a1b39713c5e252
文件大小： 2.2G
已经下载：659次
下载速度：极快
收录时间：2021-03-07
最近下载：2024-11-28

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文件列表

  • 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 98.0 MB
  • 006.Variational inference/028. Mean field approximation.mp4 81.1 MB
  • 007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 79.2 MB
  • 011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4 73.3 MB
  • 006.Variational inference/029. Example Ising model.mp4 71.5 MB
  • 004.Expectation Maximization algorithm/017. E-step details.mp4 69.5 MB
  • 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 68.6 MB
  • 005.Applications and examples/022. General EM for GMM.mp4 65.6 MB
  • 008.MCMC/041. Gibbs sampling.mp4 64.4 MB
  • 001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 62.8 MB
  • 007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 62.1 MB
  • 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 59.1 MB
  • 001.Introduction to Bayesian methods/005. Linear regression.mp4 52.5 MB
  • 009.Variational autoencoders/052. Scaling variational EM.mp4 50.1 MB
  • 008.MCMC/040. Markov Chains.mp4 49.3 MB
  • 008.MCMC/039. Sampling from 1-d distributions.mp4 49.3 MB
  • 008.MCMC/047. MCMC for LDA.mp4 49.0 MB
  • 008.MCMC/038. Monte Carlo estimation.mp4 46.7 MB
  • 008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 44.0 MB
  • 005.Applications and examples/025. Probabilistic PCA.mp4 40.9 MB
  • 011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4 38.6 MB
  • 003.Latent Variable Models/010. Latent Variable Models.mp4 38.6 MB
  • 008.MCMC/045. Example of Metropolis-Hastings.mp4 38.4 MB
  • 010.Variational Dropout/057. Dropout as Bayesian procedure.mp4 36.7 MB
  • 008.MCMC/048. Bayesian Neural Networks.mp4 35.7 MB
  • 009.Variational autoencoders/050. Modeling a distribution of images.mp4 33.8 MB
  • 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 33.5 MB
  • 003.Latent Variable Models/013. Training GMM.mp4 33.1 MB
  • 003.Latent Variable Models/014. Example of GMM training.mp4 32.8 MB
  • 011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4 32.7 MB
  • 005.Applications and examples/024. K-means, M-step.mp4 32.5 MB
  • 010.Variational Dropout/056. Learning with priors.mp4 31.9 MB
  • 008.MCMC/043. Metropolis-Hastings.mp4 31.4 MB
  • 010.Variational Dropout/058. Sparse variational dropout.mp4 31.1 MB
  • 003.Latent Variable Models/012. Gaussian Mixture Model.mp4 30.6 MB
  • 005.Applications and examples/023. K-means from probabilistic perspective.mp4 29.8 MB
  • 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 29.7 MB
  • 008.MCMC/042. Example of Gibbs sampling.mp4 28.9 MB
  • 008.MCMC/046. Markov Chain Monte Carlo summary.mp4 28.1 MB
  • 009.Variational autoencoders/055. Reparameterization trick.mp4 26.4 MB
  • 009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4 26.1 MB
  • 011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4 25.4 MB
  • 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 24.8 MB
  • 005.Applications and examples/026. EM for Probabilistic PCA.mp4 22.9 MB
  • 003.Latent Variable Models/011. Probabilistic clustering.mp4 22.8 MB
  • 009.Variational autoencoders/054. Log derivative trick.mp4 21.8 MB
  • 007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 21.5 MB
  • 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 21.3 MB
  • 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 20.4 MB
  • 009.Variational autoencoders/053. Gradient of decoder.mp4 20.2 MB
  • 004.Expectation Maximization algorithm/018. M-step details.mp4 20.1 MB
  • 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4 19.1 MB
  • 011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4 19.0 MB
  • 006.Variational inference/030. Variational EM & Review.mp4 18.2 MB
  • 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4 17.9 MB
  • 007.Latent Dirichlet Allocation/031. Topic modeling.mp4 17.6 MB
  • 011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4 17.4 MB
  • 002.Conjugate priors/008. Example Normal, precision.mp4 17.2 MB
  • 011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4 17.1 MB
  • 007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4 16.6 MB
  • 006.Variational inference/027. Why approximate inference.mp4 16.5 MB
  • 002.Conjugate priors/009. Example Bernoulli.mp4 14.7 MB
  • 002.Conjugate priors/006. Analytical inference.mp4 14.5 MB
  • 001.Introduction to Bayesian methods/003. How to define a model.mp4 10.5 MB
  • 002.Conjugate priors/007. Conjugate distributions.mp4 9.7 MB
  • 008.MCMC/047. MCMC for LDA.srt 21.3 kB
  • 009.Variational autoencoders/052. Scaling variational EM.srt 19.4 kB
  • 008.MCMC/038. Monte Carlo estimation.srt 17.3 kB
  • 006.Variational inference/029. Example Ising model.srt 17.3 kB
  • 008.MCMC/039. Sampling from 1-d distributions.srt 16.9 kB
  • 005.Applications and examples/025. Probabilistic PCA.srt 16.4 kB
  • 008.MCMC/040. Markov Chains.srt 16.1 kB
  • 003.Latent Variable Models/010. Latent Variable Models.srt 15.5 kB
  • 008.MCMC/048. Bayesian Neural Networks.srt 15.2 kB
  • 005.Applications and examples/022. General EM for GMM.srt 14.6 kB
  • 009.Variational autoencoders/050. Modeling a distribution of images.srt 14.6 kB
  • 011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt 14.1 kB
  • 003.Latent Variable Models/013. Training GMM.srt 14.1 kB
  • 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt 13.7 kB
  • 003.Latent Variable Models/014. Example of GMM training.srt 13.5 kB
  • 004.Expectation Maximization algorithm/017. E-step details.srt 13.3 kB
  • 003.Latent Variable Models/012. Gaussian Mixture Model.srt 13.2 kB
  • 008.MCMC/041. Gibbs sampling.srt 13.2 kB
  • 001.Introduction to Bayesian methods/004. Example thief & alarm.srt 12.8 kB
  • 011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt 12.8 kB
  • 008.MCMC/045. Example of Metropolis-Hastings.srt 12.8 kB
  • 008.MCMC/046. Markov Chain Monte Carlo summary.srt 12.7 kB
  • 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt 12.7 kB
  • 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt 12.2 kB
  • 006.Variational inference/028. Mean field approximation.srt 11.9 kB
  • 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt 11.9 kB
  • 001.Introduction to Bayesian methods/005. Linear regression.srt 11.5 kB
  • 005.Applications and examples/023. K-means from probabilistic perspective.srt 11.5 kB
  • 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt 10.9 kB
  • 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt 10.4 kB
  • 008.MCMC/043. Metropolis-Hastings.srt 10.0 kB
  • 009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt 9.9 kB
  • 011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt 9.9 kB
  • 011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt 9.7 kB
  • 007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt 9.7 kB
  • 009.Variational autoencoders/055. Reparameterization trick.srt 9.6 kB
  • 008.MCMC/042. Example of Gibbs sampling.srt 9.5 kB
  • 008.MCMC/044. Metropolis-Hastings choosing the critic.srt 9.4 kB
  • 010.Variational Dropout/056. Learning with priors.srt 8.9 kB
  • 005.Applications and examples/026. EM for Probabilistic PCA.srt 8.9 kB
  • 010.Variational Dropout/057. Dropout as Bayesian procedure.srt 8.5 kB
  • 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt 8.4 kB
  • 007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt 8.4 kB
  • 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt 8.3 kB
  • 003.Latent Variable Models/011. Probabilistic clustering.srt 8.2 kB
  • 004.Expectation Maximization algorithm/018. M-step details.srt 8.2 kB
  • 009.Variational autoencoders/054. Log derivative trick.srt 8.2 kB
  • 009.Variational autoencoders/053. Gradient of decoder.srt 7.8 kB
  • 006.Variational inference/030. Variational EM & Review.srt 7.8 kB
  • 010.Variational Dropout/058. Sparse variational dropout.srt 7.7 kB
  • 011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt 7.7 kB
  • 007.Latent Dirichlet Allocation/035. LDA E-step, z.srt 7.7 kB
  • 005.Applications and examples/024. K-means, M-step.srt 7.4 kB
  • 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt 7.1 kB
  • 002.Conjugate priors/008. Example Normal, precision.srt 6.9 kB
  • 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt 6.8 kB
  • 007.Latent Dirichlet Allocation/031. Topic modeling.srt 6.7 kB
  • 011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt 6.6 kB
  • 006.Variational inference/027. Why approximate inference.srt 6.4 kB
  • 007.Latent Dirichlet Allocation/037. Extensions of LDA.srt 6.3 kB
  • 011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt 6.2 kB
  • 002.Conjugate priors/009. Example Bernoulli.srt 5.6 kB
  • 002.Conjugate priors/006. Analytical inference.srt 5.0 kB
  • 001.Introduction to Bayesian methods/003. How to define a model.srt 4.2 kB
  • 002.Conjugate priors/007. Conjugate distributions.srt 3.4 kB
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