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[FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning
magnet:?xt=urn:btih:f73f5f29c36c9733b8402d09cdaae82fae990737&dn=[FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning
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f73f5f29c36c9733b8402d09cdaae82fae990737
infohash:
65
文件数量
2.2 GB
文件大小
2020-2-8 21:09
创建日期
2024-6-29 07:41
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相关分词
FreeCoursesOnline
Me
Coursera
-
Bayesian
Methods
for
Machine
Learning
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 23.69 MB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4 17.07 MB
001.Introduction to Bayesian methods/003. How to define a model.mp4 10.05 MB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 59.85 MB
001.Introduction to Bayesian methods/005. Linear regression.mp4 50.06 MB
002.Conjugate priors/006. Analytical inference.mp4 13.82 MB
002.Conjugate priors/007. Conjugate distributions.mp4 9.22 MB
002.Conjugate priors/008. Example Normal, precision.mp4 16.41 MB
002.Conjugate priors/009. Example Bernoulli.mp4 14.02 MB
003.Latent Variable Models/010. Latent Variable Models.mp4 36.78 MB
003.Latent Variable Models/011. Probabilistic clustering.mp4 21.7 MB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4 29.16 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
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 28.36 MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 31.97 MB
004.Expectation Maximization algorithm/017. E-step details.mp4 66.24 MB
004.Expectation Maximization algorithm/018. M-step details.mp4 19.21 MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 56.37 MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 65.47 MB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 20.29 MB
005.Applications and examples/022. General EM for GMM.mp4 62.53 MB
005.Applications and examples/023. K-means from probabilistic perspective.mp4 28.46 MB
005.Applications and examples/024. K-means, M-step.mp4 30.95 MB
005.Applications and examples/025. Probabilistic PCA.mp4 38.98 MB
005.Applications and examples/026. EM for Probabilistic PCA.mp4 21.8 MB
006.Variational inference/027. Why approximate inference.mp4 15.74 MB
006.Variational inference/028. Mean field approximation.mp4 77.3 MB
006.Variational inference/029. Example Ising model.mp4 68.23 MB
006.Variational inference/030. Variational EM & Review.mp4 17.38 MB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4 16.76 MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 20.49 MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4 18.22 MB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 75.56 MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 59.22 MB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 93.47 MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4 15.83 MB
008.MCMC/038. Monte Carlo estimation.mp4 44.51 MB
008.MCMC/039. Sampling from 1-d distributions.mp4 47.05 MB
008.MCMC/040. Markov Chains.mp4 47.06 MB
008.MCMC/041. Gibbs sampling.mp4 61.41 MB
008.MCMC/042. Example of Gibbs sampling.mp4 27.59 MB
008.MCMC/043. Metropolis-Hastings.mp4 29.9 MB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 42.01 MB
008.MCMC/045. Example of Metropolis-Hastings.mp4 36.61 MB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4 26.83 MB
008.MCMC/047. MCMC for LDA.mp4 46.68 MB
008.MCMC/048. Bayesian Neural Networks.mp4 34.03 MB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 19.5 MB
009.Variational autoencoders/050. Modeling a distribution of images.mp4 32.24 MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4 24.85 MB
009.Variational autoencoders/052. Scaling variational EM.mp4 47.78 MB
009.Variational autoencoders/053. Gradient of decoder.mp4 19.31 MB
009.Variational autoencoders/054. Log derivative trick.mp4 20.79 MB
009.Variational autoencoders/055. Reparameterization trick.mp4 25.18 MB
010.Variational Dropout/056. Learning with priors.mp4 30.39 MB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4 35.03 MB
010.Variational Dropout/058. Sparse variational dropout.mp4 29.61 MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4 18.16 MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4 24.18 MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4 16.36 MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4 69.86 MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4 36.81 MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4 31.23 MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4 16.61 MB
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