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001. Think bayesian & Statistics review.mp4
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MP4
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23.7 MB
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001. Think bayesian & Statistics review.srt
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SRT
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10.6 KB
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002. Bayesian approach to statistics.mp4
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MP4
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17.1 MB
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002. Bayesian approach to statistics.srt
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SRT
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6.9 KB
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003. How to define a model.mp4
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MP4
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10 MB
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003. How to define a model.srt
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SRT
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4.1 KB
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004. Example thief & alarm.mp4
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MP4
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59.8 MB
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004. Example thief & alarm.srt
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SRT
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12.5 KB
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005. Linear regression.mp4
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MP4
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50.1 MB
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005. Linear regression.srt
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SRT
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11.2 KB
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006. Analytical inference.mp4
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MP4
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13.8 MB
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006. Analytical inference.srt
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SRT
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4.9 KB
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007. Conjugate distributions.mp4
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MP4
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9.2 MB
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007. Conjugate distributions.srt
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SRT
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3.4 KB
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008. Example Normal, precision.mp4
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MP4
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16.4 MB
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008. Example Normal, precision.srt
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SRT
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6.7 KB
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009. Example Bernoulli.mp4
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MP4
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14 MB
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009. Example Bernoulli.srt
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SRT
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5.4 KB
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010. Latent Variable Models.mp4
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MP4
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36.8 MB
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010. Latent Variable Models.srt
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SRT
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15.1 KB
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011. Probabilistic clustering.mp4
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MP4
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21.7 MB
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011. Probabilistic clustering.srt
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SRT
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8 KB
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012. Gaussian Mixture Model.mp4
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MP4
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29.2 MB
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012. Gaussian Mixture Model.srt
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SRT
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12.9 KB
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013. Training GMM.mp4
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MP4
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31.6 MB
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013. Training GMM.srt
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SRT
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13.7 KB
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014. Example of GMM training.mp4
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MP4
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31.3 MB
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014. Example of GMM training.srt
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SRT
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13.1 KB
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015. Jensen's inequality & Kullback Leibler divergence.mp4
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MP4
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28.4 MB
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015. Jensen's inequality & Kullback Leibler divergence.srt
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SRT
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11.9 KB
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016. Expectation-Maximization algorithm.mp4
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MP4
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32 MB
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016. Expectation-Maximization algorithm.srt
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SRT
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13.4 KB
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017. E-step details.mp4
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MP4
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66.2 MB
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017. E-step details.srt
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SRT
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13 KB
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018. M-step details.mp4
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MP4
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19.2 MB
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018. M-step details.srt
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SRT
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8 KB
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019. Example EM for discrete mixture, E-step.mp4
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MP4
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56.4 MB
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019. Example EM for discrete mixture, E-step.srt
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SRT
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10.1 KB
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020. Example EM for discrete mixture, M-step.mp4
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MP4
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65.5 MB
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020. Example EM for discrete mixture, M-step.srt
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SRT
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12.4 KB
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021. Summary of Expectation Maximization.mp4
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MP4
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20.3 MB
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021. Summary of Expectation Maximization.srt
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SRT
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8.1 KB
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022. General EM for GMM.mp4
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MP4
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62.5 MB
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022. General EM for GMM.srt
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SRT
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14.2 KB
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023. K-means from probabilistic perspective.mp4
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MP4
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28.5 MB
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023. K-means from probabilistic perspective.srt
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SRT
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11.2 KB
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024. K-means, M-step.mp4
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MP4
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31 MB
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024. K-means, M-step.srt
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SRT
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7.2 KB
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025. Probabilistic PCA.mp4
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MP4
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39 MB
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025. Probabilistic PCA.srt
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SRT
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16 KB
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026. EM for Probabilistic PCA.mp4
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MP4
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21.8 MB
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026. EM for Probabilistic PCA.srt
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SRT
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8.7 KB
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027. Why approximate inference.mp4
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MP4
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15.7 MB
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027. Why approximate inference.srt
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SRT
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6.3 KB
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028. Mean field approximation.mp4
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MP4
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77.3 MB
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028. Mean field approximation.srt
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SRT
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11.7 KB
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029. Example Ising model.mp4
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MP4
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68.2 MB
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029. Example Ising model.srt
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SRT
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16.9 KB
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030. Variational EM & Review.mp4
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MP4
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17.4 MB
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030. Variational EM & Review.srt
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SRT
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7.6 KB
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031. Topic modeling.mp4
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MP4
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16.8 MB
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031. Topic modeling.srt
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SRT
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6.6 KB
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032. Dirichlet distribution.mp4
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MP4
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20.5 MB
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032. Dirichlet distribution.srt
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SRT
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8.2 KB
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033. Latent Dirichlet Allocation.mp4
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MP4
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18.2 MB
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033. Latent Dirichlet Allocation.srt
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SRT
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6.6 KB
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034. LDA E-step, theta.mp4
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MP4
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75.6 MB
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034. LDA E-step, theta.srt
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SRT
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9.4 KB
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035. LDA E-step, z.mp4
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MP4
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59.2 MB
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035. LDA E-step, z.srt
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SRT
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7.5 KB
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036. LDA M-step & prediction.mp4
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MP4
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93.5 MB
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036. LDA M-step & prediction.srt
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SRT
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11.6 KB
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037. Extensions of LDA.mp4
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MP4
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15.8 MB
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037. Extensions of LDA.srt
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SRT
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6.2 KB
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038. Monte Carlo estimation.mp4
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MP4
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44.5 MB
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038. Monte Carlo estimation.srt
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SRT
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16.9 KB
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039. Sampling from 1-d distributions.mp4
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MP4
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47 MB
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039. Sampling from 1-d distributions.srt
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SRT
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16.5 KB
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040. Markov Chains.mp4
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MP4
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47.1 MB
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040. Markov Chains.srt
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SRT
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15.7 KB
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041. Gibbs sampling.mp4
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MP4
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61.4 MB
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041. Gibbs sampling.srt
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SRT
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12.9 KB
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042. Example of Gibbs sampling.mp4
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MP4
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27.6 MB
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042. Example of Gibbs sampling.srt
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SRT
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9.3 KB
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043. Metropolis-Hastings.mp4
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MP4
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29.9 MB
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043. Metropolis-Hastings.srt
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SRT
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9.7 KB
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044. Metropolis-Hastings choosing the critic.mp4
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MP4
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42 MB
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044. Metropolis-Hastings choosing the critic.srt
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SRT
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9.2 KB
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045. Example of Metropolis-Hastings.mp4
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MP4
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36.6 MB
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045. Example of Metropolis-Hastings.srt
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SRT
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12.5 KB
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046. Markov Chain Monte Carlo summary.mp4
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MP4
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26.8 MB
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046. Markov Chain Monte Carlo summary.srt
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SRT
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12.4 KB
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047. MCMC for LDA.mp4
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MP4
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46.7 MB
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047. MCMC for LDA.srt
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SRT
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20.8 KB
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048. Bayesian Neural Networks.mp4
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MP4
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34 MB
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048. Bayesian Neural Networks.srt
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SRT
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14.8 KB
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049. Scaling Variational Inference & Unbiased estimates.mp4
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MP4
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19.5 MB
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049. Scaling Variational Inference & Unbiased estimates.srt
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SRT
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8.3 KB
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050. Modeling a distribution of images.mp4
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MP4
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32.2 MB
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050. Modeling a distribution of images.srt
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SRT
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14.2 KB
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051. Using CNNs with a mixture of Gaussians.mp4
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MP4
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24.9 MB
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051. Using CNNs with a mixture of Gaussians.srt
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SRT
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9.7 KB
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052. Scaling variational EM.mp4
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MP4
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47.8 MB
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052. Scaling variational EM.srt
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SRT
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18.9 KB
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053. Gradient of decoder.mp4
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MP4
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19.3 MB
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053. Gradient of decoder.srt
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SRT
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7.6 KB
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054. Log derivative trick.mp4
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MP4
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20.8 MB
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054. Log derivative trick.srt
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SRT
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8 KB
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055. Reparameterization trick.mp4
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MP4
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25.2 MB
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055. Reparameterization trick.srt
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SRT
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9.4 KB
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056. Learning with priors.mp4
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MP4
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30.4 MB
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056. Learning with priors.srt
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SRT
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8.7 KB
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057. Dropout as Bayesian procedure.mp4
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MP4
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35 MB
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057. Dropout as Bayesian procedure.srt
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SRT
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8.3 KB
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058. Sparse variational dropout.mp4
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MP4
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29.6 MB
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058. Sparse variational dropout.srt
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SRT
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7.5 KB
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059. Nonparametric methods.mp4
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MP4
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18.2 MB
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059. Nonparametric methods.srt
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SRT
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7.5 KB
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060. Gaussian processes.mp4
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MP4
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24.2 MB
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060. Gaussian processes.srt
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SRT
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9.6 KB
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061. GP for machine learning.mp4
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MP4
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16.4 MB
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061. GP for machine learning.srt
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SRT
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6.4 KB
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062. Derivation of main formula.mp4
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MP4
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69.9 MB
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062. Derivation of main formula.srt
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SRT
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9.5 KB
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063. Nuances of GP.mp4
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MP4
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36.8 MB
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063. Nuances of GP.srt
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SRT
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13.8 KB
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064. Bayesian optimization.mp4
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MP4
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31.2 MB
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064. Bayesian optimization.srt
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SRT
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12.5 KB
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065. Applications of Bayesian optimization.mp4
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MP4
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16.6 MB
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065. Applications of Bayesian optimization.srt
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SRT
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6.1 KB
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Discuss.FreeTutorials.Us.html
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HTML
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165.7 KB
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FreeCoursesOnline.Me.html
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HTML
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108.3 KB
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FreeTutorials.Eu.html
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HTML
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102.2 KB
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How you can help Team-FTU.txt
|
TXT
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307.2 B
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Torrent Downloaded From GloDls.to.txt
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TXT
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102.4 B
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[TGx]Downloaded from torrentgalaxy.org.txt
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TXT
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512 B
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![[Coursera] Bayesian Methods for Machine Learning - [FCO]](/static/img/no-torrent-image.png "[Coursera] Bayesian Methods for Machine Learning - [FCO]")
