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[FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning
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2020-2-8 21:09
2024-8-15 15:25
197
2.2 GB
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FreeCoursesOnline
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Coursera
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Bayesian
Methods
for
Machine
Learning
文件列表
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4
23.69MB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4
17.07MB
001.Introduction to Bayesian methods/003. How to define a model.mp4
10.05MB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4
59.85MB
001.Introduction to Bayesian methods/005. Linear regression.mp4
50.06MB
002.Conjugate priors/006. Analytical inference.mp4
13.82MB
002.Conjugate priors/007. Conjugate distributions.mp4
9.22MB
002.Conjugate priors/008. Example Normal, precision.mp4
16.41MB
002.Conjugate priors/009. Example Bernoulli.mp4
14.02MB
003.Latent Variable Models/010. Latent Variable Models.mp4
36.78MB
003.Latent Variable Models/011. Probabilistic clustering.mp4
21.7MB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4
29.16MB
003.Latent Variable Models/013. Training GMM.mp4
31.61MB
003.Latent Variable Models/014. Example of GMM training.mp4
31.27MB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4
28.36MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4
31.97MB
004.Expectation Maximization algorithm/017. E-step details.mp4
66.24MB
004.Expectation Maximization algorithm/018. M-step details.mp4
19.21MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4
56.37MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4
65.47MB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4
20.29MB
005.Applications and examples/022. General EM for GMM.mp4
62.53MB
005.Applications and examples/023. K-means from probabilistic perspective.mp4
28.46MB
005.Applications and examples/024. K-means, M-step.mp4
30.95MB
005.Applications and examples/025. Probabilistic PCA.mp4
38.98MB
005.Applications and examples/026. EM for Probabilistic PCA.mp4
21.8MB
006.Variational inference/027. Why approximate inference.mp4
15.74MB
006.Variational inference/028. Mean field approximation.mp4
77.3MB
006.Variational inference/029. Example Ising model.mp4
68.23MB
006.Variational inference/030. Variational EM & Review.mp4
17.38MB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4
16.76MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4
20.49MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4
18.22MB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4
75.56MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4
59.22MB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4
93.47MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4
15.83MB
008.MCMC/038. Monte Carlo estimation.mp4
44.51MB
008.MCMC/039. Sampling from 1-d distributions.mp4
47.05MB
008.MCMC/040. Markov Chains.mp4
47.06MB
008.MCMC/041. Gibbs sampling.mp4
61.41MB
008.MCMC/042. Example of Gibbs sampling.mp4
27.59MB
008.MCMC/043. Metropolis-Hastings.mp4
29.9MB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4
42.01MB
008.MCMC/045. Example of Metropolis-Hastings.mp4
36.61MB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4
26.83MB
008.MCMC/047. MCMC for LDA.mp4
46.68MB
008.MCMC/048. Bayesian Neural Networks.mp4
34.03MB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4
19.5MB
009.Variational autoencoders/050. Modeling a distribution of images.mp4
32.24MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4
24.85MB
009.Variational autoencoders/052. Scaling variational EM.mp4
47.78MB
009.Variational autoencoders/053. Gradient of decoder.mp4
19.31MB
009.Variational autoencoders/054. Log derivative trick.mp4
20.79MB
009.Variational autoencoders/055. Reparameterization trick.mp4
25.18MB
010.Variational Dropout/056. Learning with priors.mp4
30.39MB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4
35.03MB
010.Variational Dropout/058. Sparse variational dropout.mp4
29.61MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4
18.16MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4
24.18MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4
16.36MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4
69.86MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4
36.81MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4
31.23MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4
16.61MB
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