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种子名称： [FreeCoursesOnline.Me] [Coursera] Bayesian Methods for Machine Learning - [FCO]
文件类型： 视频
文件数目： 65个文件
文件大小： 2.2 GB
收录时间： 2020-9-10 12:52
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资源热度： 185
最近下载： 2024-7-2 11:12

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

001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp423.69MB

001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp417.07MB

001.Introduction to Bayesian methods/003. How to define a model.mp410.05MB

001.Introduction to Bayesian methods/004. Example thief & alarm.mp459.85MB

001.Introduction to Bayesian methods/005. Linear regression.mp450.06MB

002.Conjugate priors/006. Analytical inference.mp413.82MB

002.Conjugate priors/007. Conjugate distributions.mp49.22MB

002.Conjugate priors/008. Example Normal, precision.mp416.41MB

002.Conjugate priors/009. Example Bernoulli.mp414.02MB

003.Latent Variable Models/010. Latent Variable Models.mp436.78MB

003.Latent Variable Models/011. Probabilistic clustering.mp421.7MB

003.Latent Variable Models/012. Gaussian Mixture Model.mp429.16MB

003.Latent Variable Models/013. Training GMM.mp431.61MB

003.Latent Variable Models/014. Example of GMM training.mp431.27MB

004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp428.36MB

004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp431.97MB

004.Expectation Maximization algorithm/017. E-step details.mp466.24MB

004.Expectation Maximization algorithm/018. M-step details.mp419.21MB

004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp456.37MB

004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp465.47MB

004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp420.29MB

005.Applications and examples/022. General EM for GMM.mp462.53MB

005.Applications and examples/023. K-means from probabilistic perspective.mp428.46MB

005.Applications and examples/024. K-means, M-step.mp430.95MB

005.Applications and examples/025. Probabilistic PCA.mp438.98MB

005.Applications and examples/026. EM for Probabilistic PCA.mp421.8MB

006.Variational inference/027. Why approximate inference.mp415.74MB

006.Variational inference/028. Mean field approximation.mp477.3MB

006.Variational inference/029. Example Ising model.mp468.23MB

006.Variational inference/030. Variational EM & Review.mp417.38MB

007.Latent Dirichlet Allocation/031. Topic modeling.mp416.76MB

007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp420.49MB

007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp418.22MB

007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp475.56MB

007.Latent Dirichlet Allocation/035. LDA E-step, z.mp459.22MB

007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp493.47MB

007.Latent Dirichlet Allocation/037. Extensions of LDA.mp415.83MB

008.MCMC/038. Monte Carlo estimation.mp444.51MB

008.MCMC/039. Sampling from 1-d distributions.mp447.05MB

008.MCMC/040. Markov Chains.mp447.06MB

008.MCMC/041. Gibbs sampling.mp461.41MB

008.MCMC/042. Example of Gibbs sampling.mp427.59MB

008.MCMC/043. Metropolis-Hastings.mp429.9MB

008.MCMC/044. Metropolis-Hastings choosing the critic.mp442.01MB

008.MCMC/045. Example of Metropolis-Hastings.mp436.61MB

008.MCMC/046. Markov Chain Monte Carlo summary.mp426.83MB

008.MCMC/047. MCMC for LDA.mp446.68MB

008.MCMC/048. Bayesian Neural Networks.mp434.03MB

009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp419.5MB

009.Variational autoencoders/050. Modeling a distribution of images.mp432.24MB

009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp424.85MB

009.Variational autoencoders/052. Scaling variational EM.mp447.78MB

009.Variational autoencoders/053. Gradient of decoder.mp419.31MB

009.Variational autoencoders/054. Log derivative trick.mp420.79MB

009.Variational autoencoders/055. Reparameterization trick.mp425.18MB

010.Variational Dropout/056. Learning with priors.mp430.39MB

010.Variational Dropout/057. Dropout as Bayesian procedure.mp435.03MB

010.Variational Dropout/058. Sparse variational dropout.mp429.61MB

011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp418.16MB

011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp424.18MB

011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp416.36MB

011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp469.86MB

011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp436.81MB

011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp431.23MB

011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp416.61MB