[COURSERA] BAYESIAN METHODS FOR MACHINE LEARNING [FCO]

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[FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning 001.Introduction to Bayesian methods
  • 001. Think bayesian & Statistics review.mp4 (23.7 MB)
  • 001. Think bayesian & Statistics review.srt (10.6 KB)
  • 002. Bayesian approach to statistics.mp4 (17.1 MB)
  • 002. Bayesian approach to statistics.srt (6.9 KB)
  • 003. How to define a model.mp4 (10.0 MB)
  • 003. How to define a model.srt (4.1 KB)
  • 004. Example thief & alarm.mp4 (59.8 MB)
  • 004. Example thief & alarm.srt (12.5 KB)
  • 005. Linear regression.mp4 (50.1 MB)
  • 005. Linear regression.srt (11.2 KB)
002.Conjugate priors
  • 006. Analytical inference.mp4 (13.8 MB)
  • 006. Analytical inference.srt (4.9 KB)
  • 007. Conjugate distributions.mp4 (9.2 MB)
  • 007. Conjugate distributions.srt (3.4 KB)
  • 008. Example Normal, precision.mp4 (16.4 MB)
  • 008. Example Normal, precision.srt (6.7 KB)
  • 009. Example Bernoulli.mp4 (14.0 MB)
  • 009. Example Bernoulli.srt (5.4 KB)
003.Latent Variable Models
  • 010. Latent Variable Models.mp4 (36.8 MB)
  • 010. Latent Variable Models.srt (15.1 KB)
  • 011. Probabilistic clustering.mp4 (21.7 MB)
  • 011. Probabilistic clustering.srt (8.0 KB)
  • 012. Gaussian Mixture Model.mp4 (29.2 MB)
  • 012. Gaussian Mixture Model.srt (12.9 KB)
  • 013. Training GMM.mp4 (31.6 MB)
  • 013. Training GMM.srt (13.7 KB)
  • 014. Example of GMM training.mp4 (31.3 MB)
  • 014. Example of GMM training.srt (13.1 KB)
004.Expectation Maximization algorithm
  • 015. Jensen's inequality & Kullback Leibler divergence.mp4 (28.4 MB)
  • 015. Jensen's inequality & Kullback Leibler divergence.srt (11.9 KB)
  • 016. Expectation-Maximization algorithm.mp4 (32.0 MB)
  • 016. Expectation-Maximization algorithm.srt (13.4 KB)
  • 017. E-step details.mp4 (66.2 MB)
  • 017. E-step details.srt (13.0 KB)
  • 018. M-step details.mp4 (19.2 MB)
  • 018. M-step details.srt (8.0 KB)
  • 019. Example EM for discrete mixture, E-step.mp4 (56.4 MB)
  • 019. Example EM for discrete mixture, E-step.srt (10.1 KB)
  • 020. Example EM for discrete mixture, M-step.mp4 (65.5 MB)
  • 020. Example EM for discrete mixture, M-step.srt (12.4 KB)
  • 021. Summary of Expectation Maximization.mp4 (20.3 MB)
  • 021. Summary of Expectation Maximization.srt (8.1 KB)
005.Applications and examples
  • 022. General EM for GMM.mp4 (62.5 MB)
  • 022. General EM for GMM.srt (14.2 KB)
  • 023. K-means from probabilistic perspective.mp4 (28.5 MB)
  • 023. K-means from probabilistic perspective.srt (11.2 KB)
  • 024. K-means, M-step.mp4 (31.0 MB)
  • 024. K-means, M-step.srt (7.2 KB)
  • 025. Probabilistic PCA.mp4 (39.0 MB)
  • 025. Probabilistic PCA.srt (16.0 KB)
  • 026. EM for Probabilistic PCA.mp4 (21.8 MB)
  • 026. EM for Probabilistic PCA.srt (8.7 KB)
006.Variational inference
  • 027. Why approximate inference.mp4 (15.7 MB)
  • 027. Why approximate inference.srt (6.3 KB)
  • 028. Mean field approximation.mp4 (77.3 MB)
  • 028. Mean field approximation.srt (11.7 KB)
  • 029. Example Ising model.mp4 (68.2 MB)
  • 029. Example Ising model.srt (16.9 KB)
  • 030. Variational EM & Review.mp4 (17.4 MB)
  • 030. Variational EM & Review.srt (7.6 KB)
007.Latent Dirichlet Allocation
  • 031. Topic modeling.mp4 (16.8 MB)
  • 031. Topic modeling.srt (6.6 KB)
  • 032. Dirichlet distribution.mp4 (20.5 MB)
  • 032. Dirichlet distribution.srt (8.2 KB)
  • 033. Latent Dirichlet Allocation.mp4 (18.2 MB)
  • 033. Latent Dirichlet Allocation.srt (6.6 KB)
  • 034. LDA E-step, theta.mp4 (75.6 MB)
  • 034. LDA E-step, theta.srt (9.4 KB)
  • 035. LDA E-step, z.mp4 (59.2 MB)
  • 035. LDA E-step, z.srt (7.5 KB)
  • 036. LDA M-step & prediction.mp4 (93.5 MB)
  • 036. LDA M-step & prediction.srt (11.6 KB)
  • 037. Extensions of LDA.mp4 (15.8 MB)
  • 037. Extensions of LDA.srt (6.2 KB)
008.MCMC
  • 038. Monte Carlo estimation.mp4 (44.5 MB)
  • 038. Monte Carlo estimation.srt (16.9 KB)
  • 039. Sampling from 1-d distributions.mp4 (47.0 MB)
  • 039. Sampling from 1-d distributions.srt (16.5 KB)
  • 040. Markov Chains.mp4 (47.1 MB)
  • 040. Markov Chains.srt (15.7 KB)
  • 041. Gibbs sampling.mp4 (61.4 MB)
  • 041. Gibbs sampling.srt (12.9 KB)
  • 042. Example of Gibbs sampling.mp4 (27.6 MB)
  • 042. Example of Gibbs sampling.srt (9.3 KB)
  • 043. Metropolis-Hastings.mp4 (29.9 MB)
  • 043. Metropolis-Hastings.srt (9.7 KB)
  • 044. Metropolis-Hastings choosing the critic.mp4 (42.0 MB)
  • 044. Metropolis-Hastings choosing the critic.srt (9.2 KB)
  • 045. Example of Metropolis-Hastings.mp4 (36.6 MB)
  • 045. Example of Metropolis-Hastings.srt (12.5 KB)
  • 046. Markov Chain Monte Carlo summary.mp4 (26.8 MB)
  • 046. Markov Chain Monte Carlo summary.srt (12.4 KB)
  • 047. MCMC for LDA.mp4 (46.7 MB)
  • 047. MCMC for LDA.srt (20.8 KB)
  • 048. Bayesian Neural Networks.mp4 (34.0 MB)
  • 048. Bayesian Neural Networks.srt (14.8 KB)
009.Variational autoencoders
  • 049. Scaling Variational Inference & Unbiased estimates.mp4 (19.5 MB)
  • 049. Scaling Variational Inference & Unbiased estimates.srt (8.3 KB)
  • 050. Modeling a distribution of images.mp4 (32.2 MB)
  • 050. Modeling a distribution of images.srt (14.2 KB)
  • 051. Using CNNs with a mixture of Gaussians.mp4 (24.9 MB)
  • 051. Using CNNs with a mixture of Gaussians.srt (9.7 KB)
  • 052. Scaling variational EM.mp4 (47.8 MB)
  • 052. Scaling variational EM.srt (18.9 KB)
  • 053. Gradient of decoder.mp4 (19.3 MB)

  • Description

    [COURSERA] BAYESIAN METHODS FOR MACHINE LEARNING [FCO]

    About this course: Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can fully automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

    For more Coursera and other Courses >>> https://www.freecoursesonline.me/
    For More Udemy Free Courses >>> http://www.freetutorials.us



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[COURSERA] BAYESIAN METHODS FOR MACHINE LEARNING [FCO]

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2.2 GB
seeders:12
leechers:7
[COURSERA] BAYESIAN METHODS FOR MACHINE LEARNING [FCO]

Torrent hash: F73F5F29C36C9733B8402D09CDAAE82FAE990737