[FreeCoursesOnline.Me] [Coursera] Bayesian Methods for Machine Learning - [FCO]
大小
2.2 GB
文件数
136
Info Hash:
D39FFAB169B8717131BD5C5C511983E03FB6423B
收录时间
2025-12-17 17:35:40
更新时间
2025-12-17 17:35:40
文件列表 (136)
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4
23.69 MB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt
10.61 KB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4
17.07 MB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt
6.93 KB
001.Introduction to Bayesian methods/003. How to define a model.mp4
10.05 MB
001.Introduction to Bayesian methods/003. How to define a model.srt
4.14 KB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4
59.85 MB
001.Introduction to Bayesian methods/004. Example thief & alarm.srt
12.53 KB
001.Introduction to Bayesian methods/005. Linear regression.mp4
50.06 MB
001.Introduction to Bayesian methods/005. Linear regression.srt
11.24 KB
002.Conjugate priors/006. Analytical inference.mp4
13.82 MB
002.Conjugate priors/006. Analytical inference.srt
4.86 KB
002.Conjugate priors/007. Conjugate distributions.mp4
9.22 MB
002.Conjugate priors/007. Conjugate distributions.srt
3.37 KB
002.Conjugate priors/008. Example Normal, precision.mp4
16.41 MB
002.Conjugate priors/008. Example Normal, precision.srt
6.72 KB
002.Conjugate priors/009. Example Bernoulli.mp4
14.02 MB
002.Conjugate priors/009. Example Bernoulli.srt
5.44 KB
003.Latent Variable Models/010. Latent Variable Models.mp4
36.78 MB
003.Latent Variable Models/010. Latent Variable Models.srt
15.14 KB
003.Latent Variable Models/011. Probabilistic clustering.mp4
21.7 MB
003.Latent Variable Models/011. Probabilistic clustering.srt
8.04 KB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4
29.16 MB
003.Latent Variable Models/012. Gaussian Mixture Model.srt
12.9 KB
003.Latent Variable Models/013. Training GMM.mp4
31.61 MB
003.Latent Variable Models/013. Training GMM.srt
13.74 KB
003.Latent Variable Models/014. Example of GMM training.mp4
31.27 MB
003.Latent Variable Models/014. Example of GMM training.srt
13.15 KB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4
28.36 MB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt
11.87 KB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4
31.97 MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt
13.37 KB
004.Expectation Maximization algorithm/017. E-step details.mp4
66.24 MB
004.Expectation Maximization algorithm/017. E-step details.srt
12.96 KB
004.Expectation Maximization algorithm/018. M-step details.mp4
19.21 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
56.37 MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt
10.13 KB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4
65.47 MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt
12.37 KB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4
20.29 MB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt
8.07 KB
005.Applications and examples/022. General EM for GMM.mp4
62.53 MB
005.Applications and examples/022. General EM for GMM.srt
14.24 KB
005.Applications and examples/023. K-means from probabilistic perspective.mp4
28.46 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
30.95 MB
005.Applications and examples/024. K-means, M-step.srt
7.18 KB
005.Applications and examples/025. Probabilistic PCA.mp4
38.98 MB
005.Applications and examples/025. Probabilistic PCA.srt
16.02 KB
005.Applications and examples/026. EM for Probabilistic PCA.mp4
21.8 MB
005.Applications and examples/026. EM for Probabilistic PCA.srt
8.67 KB
006.Variational inference/027. Why approximate inference.mp4
15.74 MB
006.Variational inference/027. Why approximate inference.srt
6.28 KB
006.Variational inference/028. Mean field approximation.mp4
77.3 MB
006.Variational inference/028. Mean field approximation.srt
11.66 KB
006.Variational inference/029. Example Ising model.mp4
68.23 MB
006.Variational inference/029. Example Ising model.srt
16.86 KB
006.Variational inference/030. Variational EM & Review.mp4
17.38 MB
006.Variational inference/030. Variational EM & Review.srt
7.58 KB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4
16.76 MB
007.Latent Dirichlet Allocation/031. Topic modeling.srt
6.59 KB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4
20.49 MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt
8.17 KB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4
18.22 MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt
6.65 KB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4
75.56 MB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt
9.42 KB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4
59.22 MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.srt
7.48 KB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4
93.47 MB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt
11.63 KB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4
15.83 MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.srt
6.17 KB
008.MCMC/038. Monte Carlo estimation.mp4
44.51 MB
008.MCMC/038. Monte Carlo estimation.srt
16.89 KB
008.MCMC/039. Sampling from 1-d distributions.mp4
47.05 MB
008.MCMC/039. Sampling from 1-d distributions.srt
16.47 KB
008.MCMC/040. Markov Chains.mp4
47.06 MB
008.MCMC/040. Markov Chains.srt
15.71 KB
008.MCMC/041. Gibbs sampling.mp4
61.41 MB
008.MCMC/041. Gibbs sampling.srt
12.88 KB
008.MCMC/042. Example of Gibbs sampling.mp4
27.59 MB
008.MCMC/042. Example of Gibbs sampling.srt
9.29 KB
008.MCMC/043. Metropolis-Hastings.mp4
29.9 MB
008.MCMC/043. Metropolis-Hastings.srt
9.74 KB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4
42.01 MB
008.MCMC/044. Metropolis-Hastings choosing the critic.srt
9.19 KB
008.MCMC/045. Example of Metropolis-Hastings.mp4
36.61 MB
008.MCMC/045. Example of Metropolis-Hastings.srt
12.47 KB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4
26.83 MB
008.MCMC/046. Markov Chain Monte Carlo summary.srt
12.37 KB
008.MCMC/047. MCMC for LDA.mp4
46.68 MB
008.MCMC/047. MCMC for LDA.srt
20.83 KB
008.MCMC/048. Bayesian Neural Networks.mp4
34.03 MB
008.MCMC/048. Bayesian Neural Networks.srt
14.81 KB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4
19.5 MB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt
8.25 KB
009.Variational autoencoders/050. Modeling a distribution of images.mp4
32.24 MB
009.Variational autoencoders/050. Modeling a distribution of images.srt
14.23 KB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4
24.85 MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt
9.7 KB
009.Variational autoencoders/052. Scaling variational EM.mp4
47.78 MB
009.Variational autoencoders/052. Scaling variational EM.srt
18.92 KB
009.Variational autoencoders/053. Gradient of decoder.mp4
19.31 MB
009.Variational autoencoders/053. Gradient of decoder.srt
7.63 KB
009.Variational autoencoders/054. Log derivative trick.mp4
20.79 MB
009.Variational autoencoders/054. Log derivative trick.srt
7.98 KB
009.Variational autoencoders/055. Reparameterization trick.mp4
25.18 MB
009.Variational autoencoders/055. Reparameterization trick.srt
9.37 KB
010.Variational Dropout/056. Learning with priors.mp4
30.39 MB
010.Variational Dropout/056. Learning with priors.srt
8.72 KB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4
35.03 MB
010.Variational Dropout/057. Dropout as Bayesian procedure.srt
8.34 KB
010.Variational Dropout/058. Sparse variational dropout.mp4
29.61 MB
010.Variational Dropout/058. Sparse variational dropout.srt
7.5 KB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4
18.16 MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt
7.49 KB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4
24.18 MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt
9.63 KB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4
16.36 MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt
6.41 KB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4
69.86 MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt
9.46 KB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4
36.81 MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt
13.79 KB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4
31.23 MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt
12.53 KB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4
16.61 MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt
6.06 KB
Discuss.FreeTutorials.Us.html
165.68 KB
FreeCoursesOnline.Me.html
108.3 KB
FreeTutorials.Eu.html
102.23 KB
How you can help Team-FTU.txt
259 B
[TGx]Downloaded from torrentgalaxy.org.txt
524 B
Torrent Downloaded From GloDls.to.txt
84 B