内容简介：Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of
About the speaker
Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics. https://www.linkedin.com/in/wiggins/
About the talk
The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks require not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. The speaker will first outline how unsupervised, supervised, and reinforcement learning methods are increasingly used in human applications for description, prediction, and prescription, respectively. The speaker will then focus on the ‘prescriptive’ cases, showing how methods from the reinforcement learning and causal inference literatures can be of direct impact in engineering, business, and decision-making more generally.
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实战Java高并发程序设计
葛一鸣、郭超 / 电子工业出版社 / 2015-10-1 / CNY 69.00
在过去单核CPU时代，单任务在一个时间点只能执行单一程序，随着多核CPU的发展，并行程序开发就显得尤为重要。 《实战Java高并发程序设计》主要介绍基于Java的并行程序设计基础、思路、方法和实战。第一，立足于并发程序基础，详细介绍Java中进行并行程序设计的基本方法。第二，进一步详细介绍JDK中对并行程序的强大支持，帮助读者快速、稳健地进行并行程序开发。第三，详细讨论有关“锁”的优化和提高......一起来看看 《实战Java高并发程序设计》 这本书的介绍吧!
