Hi! In the following posts, I will introduce Q-Learning, the first part to learn if you want to pick up reinforcement learning. But before that, let us shed light on some fundamental concepts in reinforcement learning (RL). Kindergarten Example Q-Learning works in this way: do an action and get reward and observation from the environment,Continue reading “Reinforcement Learning (1): Q-Learning basics”
Recently working on a shared task job of image annotation. An interesting paper saw on NIPS’15 was proposed by Baidu Research. Find paper here . Official website. This post is the study notes.
About Decision Trees * All samples will start from the root. * At each node, one feature will split the samples.
About advanced machine learning:
GraphComputer TP3 provides OLTP and OLAP means of interacting with a graph. OLTP-based graph system provides query in real-time, with a limited set of data and respond on the order of milliseconds or seconds. (Only a part). The graph is walked by moving from vertex to another, via incident edges.
Graph Coloring Algorithms
Logistic Regression is very popular in Machine Learning, used to give predictions on something. (It is not the exact probabilities, but general values. )
Parallel in Variables (Vertexes): General huge, undirected graph: each vertex is a variable (parallel sampling on a high dimension).
About BN Belief Network, or directed acyclic graphical model (DAG). When BN is huge: Exact Inference(variable elimination) Stochastic Inference(MCMC)
Pregel: Message Passing. Focus on the process, no matter each vertex computation. Steps : (this part was referenced from a blog)