Skip to main content

Posts

Showing posts from March 26, 2017
Update: New codes available listed below for New customers NEWAEUSER $15 off $55 (6000 uses) NEWAEBUYER $7 off $15 (4000 uses) AENEW $15 off $70 (20000 uses) Aliexpress.com has for New Customers: $7 off orders $15+ or $15 off orders $70+ when you follow the instructions below. Shipping will vary. Thanks iconian Click here Choose coupon code that best suits your order: Enter coupon code NEWAEUSER + captcha for $15 off $55+ orders Enter coupon code NEWAEBUYER + captcha for $7 off $15+ orders Enter coupon code AENEW + captcha for $15 off $70+ orders Login or create your account Re-enter the coupons to add them to your account. Once you checkout, you can apply the code at the bottom of the screen Note, you may need to re-apply codes at various stages Shop Aliexpress Link Note, some exclusions may apply

Kubernetes vs Swarm

Kubernetes is a more mature and powerful orchestration tool than Swarm. Swarm provides basic and essential native clustering capabilities. But Kubernetes has built-in self-healing, service discovery (etcd), load balancing, automated rollouts and rollbacks, etc. Building all these functions on Swarm is not trivial. However, this may or may not be a good thing depending on use cases. If you do need all the features that Kubernetes provides and don't intend to do any customization, Kubernetes is perfect for you. Otherwise, the complexity of Kubernetes might become a burden because it requires more efforts to adopt and support. Different philosophies. Kubernetes has clearly taken an all-in-one approach, while Swarm is  batteries included but swappable . So if I want to use Consul as the service discovery backend, I can easily do that in Swarm. But Kubernetes uses etcd by default and  it's still not supported after more than one year. . Kubernetes is primarily based on Google...

标 题: 形势比人强 一年转行找工作的总结体会

发信人: santa (想回到过去), 信区: JobHunting 标  题: 形势比人强  一年转行找工作的总结体会 发信站: BBS 未名空间站 (Mon Mar 27 12:06:12 2017, 美东) 长达一年的转行加找工作经历结束了,和大家分享下经验教训 背景: midwest一州立大学 实验物理PhD 然后在东海岸另一州立学校博后3年 无绿卡 有H4 EAD 全部自学   onsite面试了google, facebook, Fannie Mae, Freddie Mac, Capital One, comscore和两个小公司的SDE,data scientist, stats还有quant职位 电话面试挂了amazon 和 Bloomberg 最后收到一个quant offer 几点感想: 1. 首先一定要有信心,大部分理工科PhD应付现在这些热门专业其实都绰绰有余,当然 你得花时间,要少走弯路,要多刷题,这些热门方向都是相通的,总体就是你要有 programming和数学(linear algebra, stats)的基础,有些专业大二后就很少接触数学 和编程 或者这两者都不是你的comfort zone 这样会要多花些时间  但我觉得只要你国 内能上一本就不会有问题 当然还要加上一些communication和一些corporate culture 的soft skills 2. 刷题要三遍! 个人刷题不到一遍,而且当初刷的是九章算法推荐的lintcode不是 leetcode  最后面刷题的google facebook都挂了 而且都倒在了leetcode有lintcode没 有的题目 所以强调刷题要三遍 你在现场的气氛是几乎不可能很快把一道你没见过的题 目写出来的 而且要很熟练白板写题 这样看来flag的bar我觉得还是很高 但对转行者没 经验不造成歧视 3. 一些我用过的网上资源评价: 3.1 coursera的JHU的data science specialization: 推荐,尤其是你对data analytics还一无所知的时候,可以帮助你熟悉基本概念还有R 3.2 coursera的UW的machine learning...