BlinkDB[1] uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query’s accuracy or response time requirements.

Reinforcement Learning发展至今日已无法进行非常明确而清晰的分类，很多新方法也是博采众长。不过，为了更好的了解强化学习算法主要学习什么以及怎么学习，本文尝试从不同角度对主要的RL算法进行大致分类。

强化学习[Reinforcement Learning]是一个独立的机器学习研究领域，主要任务是根据不同的环境通过训练机器学习的模型来进行一系列决策。

Here are the most important software design principles discussed in this book.

This blog discuss several principles and tips about refactoring the existing code to eliminate some problems.

Chapter 4 argued that modules should be deep. Chapter 5, 6 discuss techniques for creating deep modules.

使用 Python 时，经常会遇到的问题是

TensorFlow 2.x在如火如荼的迭代，1.15.4是TF 1.x的稳定版本。本文介绍在Ubuntu和virtualenv的环境下的tensorflow 1.15.4源码安装过程。源码安装的一个好处是可以自定义cuda和cudnn的次级版本号，比如TF 1.15.4默认支持CUDA 10，但是pip上的预安装的tensorflow 1.15.4是基于10.0，源码安装可以自定义到10.2。