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

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File Name
Size
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4
24 MB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt
11 kB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4
17 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
60 MB
001.Introduction to Bayesian methods/004. Example thief & alarm.srt
12 kB
001.Introduction to Bayesian methods/005. Linear regression.mp4
50 MB
001.Introduction to Bayesian methods/005. Linear regression.srt
11 kB
002.Conjugate priors/006. Analytical inference.mp4
14 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 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
37 MB
003.Latent Variable Models/010. Latent Variable Models.srt
15 kB
003.Latent Variable Models/011. Probabilistic clustering.mp4
22 MB
003.Latent Variable Models/011. Probabilistic clustering.srt
8.0 kB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4
29 MB
003.Latent Variable Models/012. Gaussian Mixture Model.srt
13 kB
003.Latent Variable Models/013. Training GMM.mp4
32 MB
003.Latent Variable Models/013. Training GMM.srt
14 kB
003.Latent Variable Models/014. Example of GMM training.mp4
31 MB
003.Latent Variable Models/014. Example of GMM training.srt
13 kB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4
28 MB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt
12 kB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4
32 MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt
13 kB
004.Expectation Maximization algorithm/017. E-step details.mp4
66 MB
004.Expectation Maximization algorithm/017. E-step details.srt
13 kB
004.Expectation Maximization algorithm/018. M-step details.mp4
19 MB
004.Expectation Maximization algorithm/018. M-step details.srt
8.0 kB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4
56 MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt
10 kB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4
66 MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt
12 kB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4
20 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 MB
005.Applications and examples/022. General EM for GMM.srt
14 kB
005.Applications and examples/023. K-means from probabilistic perspective.mp4
28 MB
005.Applications and examples/023. K-means from probabilistic perspective.srt
11 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
22 MB
005.Applications and examples/026. EM for Probabilistic PCA.srt
8.7 kB
006.Variational inference/027. Why approximate inference.mp4
16 MB
006.Variational inference/027. Why approximate inference.srt
6.3 kB
006.Variational inference/028. Mean field approximation.mp4
77 MB
006.Variational inference/028. Mean field approximation.srt
12 kB
006.Variational inference/029. Example Ising model.mp4
68 MB
006.Variational inference/029. Example Ising model.srt
17 kB
006.Variational inference/030. Variational EM & Review.mp4
17 MB
006.Variational inference/030. Variational EM & Review.srt
7.6 kB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4
17 MB
007.Latent Dirichlet Allocation/031. Topic modeling.srt
6.6 kB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4
20 MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt
8.2 kB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4
18 MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt
6.6 kB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4
76 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 MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.srt
7.5 kB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4
94 MB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt
12 kB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4
16 MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.srt
6.2 kB
008.MCMC/038. Monte Carlo estimation.mp4
44 MB
008.MCMC/038. Monte Carlo estimation.srt
17 kB
008.MCMC/039. Sampling from 1-d distributions.mp4
47 MB
008.MCMC/039. Sampling from 1-d distributions.srt
16 kB
008.MCMC/040. Markov Chains.mp4
47 MB
008.MCMC/040. Markov Chains.srt
16 kB
008.MCMC/041. Gibbs sampling.mp4
61 MB
008.MCMC/041. Gibbs sampling.srt
13 kB
008.MCMC/042. Example of Gibbs sampling.mp4
28 MB
008.MCMC/042. Example of Gibbs sampling.srt
9.3 kB
008.MCMC/043. Metropolis-Hastings.mp4
30 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
37 MB
008.MCMC/045. Example of Metropolis-Hastings.srt
12 kB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4
27 MB
008.MCMC/046. Markov Chain Monte Carlo summary.srt
12 kB
008.MCMC/047. MCMC for LDA.mp4
47 MB
008.MCMC/047. MCMC for LDA.srt
21 kB
008.MCMC/048. Bayesian Neural Networks.mp4
34 MB
008.MCMC/048. Bayesian Neural Networks.srt
15 kB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4
20 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 MB
009.Variational autoencoders/050. Modeling a distribution of images.srt
14 kB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4
25 MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt
9.7 kB
009.Variational autoencoders/052. Scaling variational EM.mp4
48 MB
009.Variational autoencoders/052. Scaling variational EM.srt
19 kB
009.Variational autoencoders/053. Gradient of decoder.mp4
19 MB
009.Variational autoencoders/053. Gradient of decoder.srt
7.6 kB
009.Variational autoencoders/054. Log derivative trick.mp4
21 MB
009.Variational autoencoders/054. Log derivative trick.srt
8.0 kB
009.Variational autoencoders/055. Reparameterization trick.mp4
25 MB
009.Variational autoencoders/055. Reparameterization trick.srt
9.4 kB
010.Variational Dropout/056. Learning with priors.mp4
30 MB
010.Variational Dropout/056. Learning with priors.srt
8.7 kB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4
35 MB
010.Variational Dropout/057. Dropout as Bayesian procedure.srt
8.3 kB
010.Variational Dropout/058. Sparse variational dropout.mp4
30 MB
010.Variational Dropout/058. Sparse variational dropout.srt
7.5 kB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4
18 MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt
7.5 kB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4
24 MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt
9.6 kB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4
16 MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt
6.4 kB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4
70 MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt
9.5 kB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4
37 MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt
14 kB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4
31 MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt
12 kB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4
17 MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt
6.1 kB
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119 B
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252 B