001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4
23.69MB
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001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt
10.61KB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4
17.07MB
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001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt
6.93KB
001.Introduction to Bayesian methods/003. How to define a model.mp4
10.05MB
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001.Introduction to Bayesian methods/003. How to define a model.srt
4.14KB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4
59.85MB
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001.Introduction to Bayesian methods/004. Example thief & alarm.srt
12.53KB
001.Introduction to Bayesian methods/005. Linear regression.mp4
50.06MB
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001.Introduction to Bayesian methods/005. Linear regression.srt
11.24KB
002.Conjugate priors/006. Analytical inference.mp4
13.82MB
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002.Conjugate priors/006. Analytical inference.srt
4.86KB
002.Conjugate priors/007. Conjugate distributions.mp4
9.22MB
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002.Conjugate priors/007. Conjugate distributions.srt
3.37KB
002.Conjugate priors/008. Example Normal, precision.mp4
16.41MB
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002.Conjugate priors/008. Example Normal, precision.srt
6.72KB
002.Conjugate priors/009. Example Bernoulli.mp4
14.02MB
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002.Conjugate priors/009. Example Bernoulli.srt
5.44KB
003.Latent Variable Models/010. Latent Variable Models.mp4
36.78MB
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003.Latent Variable Models/010. Latent Variable Models.srt
15.14KB
003.Latent Variable Models/011. Probabilistic clustering.mp4
21.7MB
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003.Latent Variable Models/011. Probabilistic clustering.srt
8.04KB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4
29.16MB
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003.Latent Variable Models/012. Gaussian Mixture Model.srt
12.9KB
003.Latent Variable Models/013. Training GMM.mp4
31.61MB
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003.Latent Variable Models/013. Training GMM.srt
13.74KB
003.Latent Variable Models/014. Example of GMM training.mp4
31.27MB
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003.Latent Variable Models/014. Example of GMM training.srt
13.15KB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4
28.36MB
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004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt
11.87KB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4
31.97MB
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004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt
13.37KB
004.Expectation Maximization algorithm/017. E-step details.mp4
66.24MB
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004.Expectation Maximization algorithm/017. E-step details.srt
12.96KB
004.Expectation Maximization algorithm/018. M-step details.mp4
19.21MB
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004.Expectation Maximization algorithm/018. M-step details.srt
8KB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4
56.37MB
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004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt
10.13KB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4
65.47MB
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004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt
12.37KB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4
20.29MB
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004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt
8.07KB
005.Applications and examples/022. General EM for GMM.mp4
62.53MB
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005.Applications and examples/022. General EM for GMM.srt
14.24KB
005.Applications and examples/023. K-means from probabilistic perspective.mp4
28.46MB
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005.Applications and examples/023. K-means from probabilistic perspective.srt
11.2KB
005.Applications and examples/024. K-means, M-step.mp4
30.95MB
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005.Applications and examples/024. K-means, M-step.srt
7.18KB
005.Applications and examples/025. Probabilistic PCA.mp4
38.98MB
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005.Applications and examples/025. Probabilistic PCA.srt
16.02KB
005.Applications and examples/026. EM for Probabilistic PCA.mp4
21.8MB
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005.Applications and examples/026. EM for Probabilistic PCA.srt
8.67KB
006.Variational inference/027. Why approximate inference.mp4
15.74MB
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006.Variational inference/027. Why approximate inference.srt
6.28KB
006.Variational inference/028. Mean field approximation.mp4
77.3MB
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006.Variational inference/028. Mean field approximation.srt
11.66KB
006.Variational inference/029. Example Ising model.mp4
68.23MB
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006.Variational inference/029. Example Ising model.srt
16.86KB
006.Variational inference/030. Variational EM & Review.mp4
17.38MB
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006.Variational inference/030. Variational EM & Review.srt
7.58KB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4
16.76MB
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007.Latent Dirichlet Allocation/031. Topic modeling.srt
6.59KB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4
20.49MB
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007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt
8.17KB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4
18.22MB
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007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt
6.65KB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4
75.56MB
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007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt
9.42KB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4
59.22MB
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007.Latent Dirichlet Allocation/035. LDA E-step, z.srt
7.48KB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4
93.47MB
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007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt
11.63KB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4
15.83MB
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007.Latent Dirichlet Allocation/037. Extensions of LDA.srt
6.17KB
008.MCMC/038. Monte Carlo estimation.mp4
44.51MB
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008.MCMC/038. Monte Carlo estimation.srt
16.89KB
008.MCMC/039. Sampling from 1-d distributions.mp4
47.05MB
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008.MCMC/039. Sampling from 1-d distributions.srt
16.47KB
008.MCMC/040. Markov Chains.mp4
47.06MB
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008.MCMC/040. Markov Chains.srt
15.71KB
008.MCMC/041. Gibbs sampling.mp4
61.41MB
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008.MCMC/041. Gibbs sampling.srt
12.88KB
008.MCMC/042. Example of Gibbs sampling.mp4
27.59MB
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008.MCMC/042. Example of Gibbs sampling.srt
9.29KB
008.MCMC/043. Metropolis-Hastings.mp4
29.9MB
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008.MCMC/043. Metropolis-Hastings.srt
9.74KB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4
42.01MB
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008.MCMC/044. Metropolis-Hastings choosing the critic.srt
9.19KB
008.MCMC/045. Example of Metropolis-Hastings.mp4
36.61MB
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008.MCMC/045. Example of Metropolis-Hastings.srt
12.47KB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4
26.83MB
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008.MCMC/046. Markov Chain Monte Carlo summary.srt
12.37KB
008.MCMC/047. MCMC for LDA.mp4
46.68MB
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008.MCMC/047. MCMC for LDA.srt
20.83KB
008.MCMC/048. Bayesian Neural Networks.mp4
34.03MB
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008.MCMC/048. Bayesian Neural Networks.srt
14.81KB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4
19.5MB
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009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt
8.25KB
009.Variational autoencoders/050. Modeling a distribution of images.mp4
32.24MB
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009.Variational autoencoders/050. Modeling a distribution of images.srt
14.23KB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4
24.85MB
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009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt
9.7KB
009.Variational autoencoders/052. Scaling variational EM.mp4
47.78MB
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009.Variational autoencoders/052. Scaling variational EM.srt
18.92KB
009.Variational autoencoders/053. Gradient of decoder.mp4
19.31MB
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009.Variational autoencoders/053. Gradient of decoder.srt
7.63KB
009.Variational autoencoders/054. Log derivative trick.mp4
20.79MB
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009.Variational autoencoders/054. Log derivative trick.srt
7.98KB
009.Variational autoencoders/055. Reparameterization trick.mp4
25.18MB
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009.Variational autoencoders/055. Reparameterization trick.srt
9.37KB
010.Variational Dropout/056. Learning with priors.mp4
30.39MB
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010.Variational Dropout/056. Learning with priors.srt
8.72KB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4
35.03MB
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010.Variational Dropout/057. Dropout as Bayesian procedure.srt
8.34KB
010.Variational Dropout/058. Sparse variational dropout.mp4
29.61MB
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010.Variational Dropout/058. Sparse variational dropout.srt
7.5KB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4
18.16MB
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011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt
7.49KB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4
24.18MB
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011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt
9.63KB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4
16.36MB
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011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt
6.41KB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4
69.86MB
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011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt
9.46KB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4
36.81MB
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011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt
13.79KB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4
31.23MB
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011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt
12.53KB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4
16.61MB
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011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt
6.06KB
Discuss.FreeTutorials.Us.html
165.68KB
FreeCoursesOnline.Me.html
108.3KB
FreeTutorials.Eu.html
102.23KB
How you can help Team-FTU.txt
259B
[TGx]Downloaded from torrentgalaxy.org.txt
524B
Torrent Downloaded From GloDls.to.txt
84B