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-# bayes-nn
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+# Understanding Bayesian Deep Learning 
+
+1. Elementary mathematics
+- Set theory
+- Measure theory
+- Probability
+- Random variable
+- Random process
+- Functional analysis (harmonic analysis)
+
+2. Gaussian process
+- Gaussian process
+- Weight space view
+- Function space view
+- Gaussian process latent variable model 
+
+3. Bayesian neural netwrok
+- Minimum description length
+- Ensemble learning in Bayesian neural network
+- Practical variational inference
+- Bayes by backprop
+- Summary of variational inference
+- Dropout as a Bayesian approximation
+- Stein variational gradient descent