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007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 93.47 MB
006.Variational inference/028. Mean field approximation.mp4 77.3 MB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 75.56 MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4 69.86 MB
006.Variational inference/029. Example Ising model.mp4 68.23 MB
004.Expectation Maximization algorithm/017. E-step details.mp4 66.24 MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 65.47 MB
005.Applications and examples/022. General EM for GMM.mp4 62.53 MB
008.MCMC/041. Gibbs sampling.mp4 61.41 MB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 59.85 MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 59.22 MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 56.37 MB
001.Introduction to Bayesian methods/005. Linear regression.mp4 50.06 MB
009.Variational autoencoders/052. Scaling variational EM.mp4 47.78 MB
008.MCMC/040. Markov Chains.mp4 47.06 MB
008.MCMC/039. Sampling from 1-d distributions.mp4 47.05 MB
008.MCMC/047. MCMC for LDA.mp4 46.68 MB
008.MCMC/038. Monte Carlo estimation.mp4 44.51 MB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 42.01 MB
005.Applications and examples/025. Probabilistic PCA.mp4 38.98 MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4 36.81 MB
003.Latent Variable Models/010. Latent Variable Models.mp4 36.78 MB
008.MCMC/045. Example of Metropolis-Hastings.mp4 36.61 MB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4 35.03 MB
008.MCMC/048. Bayesian Neural Networks.mp4 34.03 MB
009.Variational autoencoders/050. Modeling a distribution of images.mp4 32.24 MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 31.97 MB
003.Latent Variable Models/013. Training GMM.mp4 31.61 MB
003.Latent Variable Models/014. Example of GMM training.mp4 31.27 MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4 31.23 MB
005.Applications and examples/024. K-means, M-step.mp4 30.95 MB
010.Variational Dropout/056. Learning with priors.mp4 30.39 MB
008.MCMC/043. Metropolis-Hastings.mp4 29.9 MB
010.Variational Dropout/058. Sparse variational dropout.mp4 29.61 MB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4 29.16 MB
005.Applications and examples/023. K-means from probabilistic perspective.mp4 28.46 MB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 28.36 MB
008.MCMC/042. Example of Gibbs sampling.mp4 27.59 MB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4 26.83 MB
009.Variational autoencoders/055. Reparameterization trick.mp4 25.18 MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4 24.85 MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4 24.18 MB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 23.69 MB
005.Applications and examples/026. EM for Probabilistic PCA.mp4 21.8 MB
003.Latent Variable Models/011. Probabilistic clustering.mp4 21.7 MB
009.Variational autoencoders/054. Log derivative trick.mp4 20.79 MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 20.49 MB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 20.29 MB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 19.5 MB
009.Variational autoencoders/053. Gradient of decoder.mp4 19.31 MB
004.Expectation Maximization algorithm/018. M-step details.mp4 19.21 MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4 18.22 MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4 18.16 MB
006.Variational inference/030. Variational EM & Review.mp4 17.38 MB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4 17.07 MB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4 16.76 MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4 16.61 MB
002.Conjugate priors/008. Example Normal, precision.mp4 16.41 MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4 16.36 MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4 15.83 MB
006.Variational inference/027. Why approximate inference.mp4 15.74 MB
002.Conjugate priors/009. Example Bernoulli.mp4 14.02 MB
002.Conjugate priors/006. Analytical inference.mp4 13.82 MB
001.Introduction to Bayesian methods/003. How to define a model.mp4 10.05 MB
002.Conjugate priors/007. Conjugate distributions.mp4 9.22 MB
Discuss.FreeTutorials.Us.html 165.68 KB
FreeCoursesOnline.Me.html 108.3 KB
FreeTutorials.Eu.html 102.23 KB
008.MCMC/047. MCMC for LDA.srt 20.83 KB
009.Variational autoencoders/052. Scaling variational EM.srt 18.92 KB
008.MCMC/038. Monte Carlo estimation.srt 16.89 KB
006.Variational inference/029. Example Ising model.srt 16.86 KB
008.MCMC/039. Sampling from 1-d distributions.srt 16.47 KB
005.Applications and examples/025. Probabilistic PCA.srt 16.02 KB
008.MCMC/040. Markov Chains.srt 15.71 KB
003.Latent Variable Models/010. Latent Variable Models.srt 15.14 KB
008.MCMC/048. Bayesian Neural Networks.srt 14.81 KB
005.Applications and examples/022. General EM for GMM.srt 14.24 KB
009.Variational autoencoders/050. Modeling a distribution of images.srt 14.23 KB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt 13.79 KB
003.Latent Variable Models/013. Training GMM.srt 13.74 KB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt 13.37 KB
003.Latent Variable Models/014. Example of GMM training.srt 13.15 KB
004.Expectation Maximization algorithm/017. E-step details.srt 12.96 KB
003.Latent Variable Models/012. Gaussian Mixture Model.srt 12.9 KB
008.MCMC/041. Gibbs sampling.srt 12.88 KB
001.Introduction to Bayesian methods/004. Example thief & alarm.srt 12.53 KB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt 12.53 KB
008.MCMC/045. Example of Metropolis-Hastings.srt 12.47 KB
008.MCMC/046. Markov Chain Monte Carlo summary.srt 12.37 KB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt 12.37 KB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt 11.87 KB
006.Variational inference/028. Mean field approximation.srt 11.66 KB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt 11.63 KB
001.Introduction to Bayesian methods/005. Linear regression.srt 11.24 KB
005.Applications and examples/023. K-means from probabilistic perspective.srt 11.2 KB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt 10.61 KB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt 10.13 KB
008.MCMC/043. Metropolis-Hastings.srt 9.74 KB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt 9.7 KB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt 9.63 KB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt 9.46 KB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt 9.42 KB
009.Variational autoencoders/055. Reparameterization trick.srt 9.37 KB
008.MCMC/042. Example of Gibbs sampling.srt 9.29 KB
008.MCMC/044. Metropolis-Hastings choosing the critic.srt 9.19 KB
010.Variational Dropout/056. Learning with priors.srt 8.72 KB
005.Applications and examples/026. EM for Probabilistic PCA.srt 8.67 KB
010.Variational Dropout/057. Dropout as Bayesian procedure.srt 8.34 KB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt 8.25 KB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt 8.17 KB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt 8.07 KB
003.Latent Variable Models/011. Probabilistic clustering.srt 8.04 KB
004.Expectation Maximization algorithm/018. M-step details.srt 8 KB
009.Variational autoencoders/054. Log derivative trick.srt 7.98 KB
009.Variational autoencoders/053. Gradient of decoder.srt 7.63 KB
006.Variational inference/030. Variational EM & Review.srt 7.58 KB
010.Variational Dropout/058. Sparse variational dropout.srt 7.5 KB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt 7.49 KB
007.Latent Dirichlet Allocation/035. LDA E-step, z.srt 7.48 KB
005.Applications and examples/024. K-means, M-step.srt 7.18 KB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt 6.93 KB
002.Conjugate priors/008. Example Normal, precision.srt 6.72 KB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt 6.65 KB
007.Latent Dirichlet Allocation/031. Topic modeling.srt 6.59 KB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt 6.41 KB
006.Variational inference/027. Why approximate inference.srt 6.28 KB
007.Latent Dirichlet Allocation/037. Extensions of LDA.srt 6.17 KB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt 6.06 KB
002.Conjugate priors/009. Example Bernoulli.srt 5.44 KB
002.Conjugate priors/006. Analytical inference.srt 4.86 KB
001.Introduction to Bayesian methods/003. How to define a model.srt 4.14 KB
002.Conjugate priors/007. Conjugate distributions.srt 3.37 KB
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How you can help Team-FTU.txt 259 B
Torrent Downloaded From GloDls.to.txt 84 B
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