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# bayes-nn | ||
# Understanding Bayesian Deep Learning | ||
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1. Elementary mathematics | ||
- Set theory | ||
- Measure theory | ||
- Probability | ||
- Random variable | ||
- Random process | ||
- Functional analysis (harmonic analysis) | ||
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2. Gaussian process | ||
- Gaussian process | ||
- Weight space view | ||
- Function space view | ||
- Gaussian process latent variable model | ||
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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 |