项目作者： WilliamYi96
项目描述 ：
Generative Models
高级语言：
项目地址: git://github.com/WilliamYi96/Variational-Inference.git
Variational-Inference
The beginning of my journey of Approximate Bayesian Inference
Step One: Start the Journey
Basic Concepts
- Probabilisity Theory
<>([pdf(more detailed)]) - Convex Optimization
Resources
- Machine Learning Course — Stanford CS229, containing the basic concepts of ML.
- UCI small dataset, it contains a large collection of standard datasets for testing learning algorithms.
Books for Reference
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference [source code] [Note: Suggest reading through!]
- Convex Optimization [Note: A little difficult to read, good for reference concerning convex optmization]
Step Two: Understand Basic Concepts of VI
- From MLE (Maximum Likelihood Estimation) to EM (Expectation Maximization) [blog-cn] [cs229—more theoretical]
Step Three: Paper Reading
- Advances in Variational Inference. [notes] [arkiv]
- Bayesian Dark Knowledge. [notes] [arkiv]
