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

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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.

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File list
  • [FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning
  • 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 23.7 MB
  • 001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt 10.6 KB
  • 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4 17.1 MB
  • 001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt 6.9 KB
  • 001.Introduction to Bayesian methods/003. How to define a model.mp4 10 MB
  • 001.Introduction to Bayesian methods/003. How to define a model.srt 4.1 KB
  • 001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 59.8 MB
  • 001.Introduction to Bayesian methods/004. Example thief & alarm.srt 12.5 KB
  • 001.Introduction to Bayesian methods/005. Linear regression.mp4 50.1 MB
  • 001.Introduction to Bayesian methods/005. Linear regression.srt 11.2 KB
  • 002.Conjugate priors/006. Analytical inference.mp4 13.8 MB
  • 002.Conjugate priors/006. Analytical inference.srt 4.9 KB
  • 002.Conjugate priors/007. Conjugate distributions.mp4 9.2 MB
  • 002.Conjugate priors/007. Conjugate distributions.srt 3.4 KB
  • 002.Conjugate priors/008. Example Normal, precision.mp4 16.4 MB
  • 002.Conjugate priors/008. Example Normal, precision.srt 6.7 KB
  • 002.Conjugate priors/009. Example Bernoulli.mp4 14 MB
  • 002.Conjugate priors/009. Example Bernoulli.srt 5.4 KB
  • 003.Latent Variable Models/010. Latent Variable Models.mp4 36.8 MB
  • 003.Latent Variable Models/010. Latent Variable Models.srt 15.1 KB
  • 003.Latent Variable Models/011. Probabilistic clustering.mp4 21.7 MB
  • 003.Latent Variable Models/011. Probabilistic clustering.srt 8 KB
  • 003.Latent Variable Models/012. Gaussian Mixture Model.mp4 29.2 MB
  • 003.Latent Variable Models/012. Gaussian Mixture Model.srt 12.9 KB
  • 003.Latent Variable Models/013. Training GMM.mp4 31.6 MB
  • 003.Latent Variable Models/013. Training GMM.srt 13.7 KB
  • 003.Latent Variable Models/014. Example of GMM training.mp4 31.3 MB
  • 003.Latent Variable Models/014. Example of GMM training.srt 13.1 KB
  • 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 28.4 MB
  • 004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt 11.9 KB
  • 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 32 MB
  • 004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt 13.4 KB
  • 004.Expectation Maximization algorithm/017. E-step details.mp4 66.2 MB
  • 004.Expectation Maximization algorithm/017. E-step details.srt 13 KB
  • 004.Expectation Maximization algorithm/018. M-step details.mp4 19.2 MB
  • 004.Expectation Maximization algorithm/018. M-step details.srt 8 KB
  • 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 56.4 MB
  • 004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt 10.1 KB
  • 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 65.5 MB
  • 004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt 12.4 KB
  • 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 20.3 MB
  • 004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt 8.1 KB
  • 005.Applications and examples/022. General EM for GMM.mp4 62.5 MB
  • 005.Applications and examples/022. General EM for GMM.srt 14.2 KB
  • 005.Applications and examples/023. K-means from probabilistic perspective.mp4 28.5 MB
  • 005.Applications and examples/023. K-means from probabilistic perspective.srt 11.2 KB
  • 005.Applications and examples/024. K-means, M-step.mp4 31 MB
  • 005.Applications and examples/024. K-means, M-step.srt 7.2 KB
  • 005.Applications and examples/025. Probabilistic PCA.mp4 39 MB
  • 005.Applications and examples/025. Probabilistic PCA.srt 16 KB
  • 005.Applications and examples/026. EM for Probabilistic PCA.mp4 21.8 MB
  • 005.Applications and examples/026. EM for Probabilistic PCA.srt 8.7 KB
  • 006.Variational inference/027. Why approximate inference.mp4 15.7 MB
  • 006.Variational inference/027. Why approximate inference.srt 6.3 KB
  • 006.Variational inference/028. Mean field approximation.mp4 77.3 MB
  • 006.Variational inference/028. Mean field approximation.srt 11.7 KB
  • 006.Variational inference/029. Example Ising model.mp4 68.2 MB
  • 006.Variational inference/029. Example Ising model.srt 16.9 KB
  • 006.Variational inference/030. Variational EM & Review.mp4 17.4 MB
  • 006.Variational inference/030. Variational EM & Review.srt 7.6 KB
  • 007.Latent Dirichlet Allocation/031. Topic modeling.mp4 16.8 MB
  • 007.Latent Dirichlet Allocation/031. Topic modeling.srt 6.6 KB
  • 007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 20.5 MB
  • 007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt 8.2 KB
  • 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4 18.2 MB
  • 007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt 6.6 KB
  • 007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 75.6 MB
  • 007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt 9.4 KB
  • 007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 59.2 MB
  • 007.Latent Dirichlet Allocation/035. LDA E-step, z.srt 7.5 KB
  • 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 93.5 MB
  • 007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt 11.6 KB
  • 007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4 15.8 MB
  • 007.Latent Dirichlet Allocation/037. Extensions of LDA.srt 6.2 KB
  • 008.MCMC/038. Monte Carlo estimation.mp4 44.5 MB
  • 008.MCMC/038. Monte Carlo estimation.srt 16.9 KB
  • 008.MCMC/039. Sampling from 1-d distributions.mp4 47 MB
  • 008.MCMC/039. Sampling from 1-d distributions.srt 16.5 KB
  • 008.MCMC/040. Markov Chains.mp4 47.1 MB
  • 008.MCMC/040. Markov Chains.srt 15.7 KB
  • 008.MCMC/041. Gibbs sampling.mp4 61.4 MB
  • 008.MCMC/041. Gibbs sampling.srt 12.9 KB
  • 008.MCMC/042. Example of Gibbs sampling.mp4 27.6 MB
  • 008.MCMC/042. Example of Gibbs sampling.srt 9.3 KB
  • 008.MCMC/043. Metropolis-Hastings.mp4 29.9 MB
  • 008.MCMC/043. Metropolis-Hastings.srt 9.7 KB
  • 008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 42 MB
  • 008.MCMC/044. Metropolis-Hastings choosing the critic.srt 9.2 KB
  • 008.MCMC/045. Example of Metropolis-Hastings.mp4 36.6 MB
  • 008.MCMC/045. Example of Metropolis-Hastings.srt 12.5 KB
  • 008.MCMC/046. Markov Chain Monte Carlo summary.mp4 26.8 MB
  • 008.MCMC/046. Markov Chain Monte Carlo summary.srt 12.4 KB
  • 008.MCMC/047. MCMC for LDA.mp4 46.7 MB
  • 008.MCMC/047. MCMC for LDA.srt 20.8 KB
  • 008.MCMC/048. Bayesian Neural Networks.mp4 34 MB
  • 008.MCMC/048. Bayesian Neural Networks.srt 14.8 KB
  • 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 19.5 MB
  • 009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt 8.3 KB
  • 009.Variational autoencoders/050. Modeling a distribution of images.mp4 32.2 MB

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