The first choice for payment in future,Magi or Sether?

本文探讨了Magi和Sether两个新兴项目在电子支付领域的应用与潜力。Magi致力于提供低成本、高能效的挖矿解决方案,并通过MagiPay实现便捷支付。Sether则利用智能合约提供透明营销环境及绩效驱动的支付系统。

In a series of commercial activities, it is the ultimate goal of all business activities to conclude a transaction and complete payment. Since ancient times, the world has been trying to find a better way to make money and pay.

After the financial crisis, bitcoin and ripple, the digital currency, made a huge contribution to innovation.Of course, with precedent, the new blockchain project also began to serve payments, such as magi and sether.

The currency is the most convenient digital currency in the use phase of all currencies, from the physical, the gold and silver to the notes to the digital currency. An ancient digital currency, Denarius, is used by eight countries in payment. Although the situation of denarius coin for sale is rare, most people use it to exchange other currencies for indirect payment.Bitcoin and ripple, two familiar digital coins to the public, offer an easy access in payment. The following are two potential projects, magi and sether.

what is magi?


Magi is an electronic payment solution. Magin uses cost-efficient mining equipment to protect its low-cost shielding systems.

Magin hopes to provide mining to everyone. Because, Magi is in the pursuit of fairness, low cost and high energy efficiency at the same time. Magi aims to offer an opportunity to compete fairly without buying expensive hardware mining through removing the essence of mining competition.

Magi program and magi coin(for short: XMG) are suitable for any person in the world, and people can use Magi coin to freely pay.

XMG is quite developed, it is not only communication between support multiple currencies, and xmg potential is very big also, although the rankings in 771, the price of Magic coin had a glorious history, such as in January, its price is $1.75.

When using digital money to pay, there will be a payment gateway. MagiPay is the payment gateway for XMG. Two of the most immediate reasons for the xmgi project to develop MagiPay are that magi is used in a relatively simple way. Another is that MagiPay is an independent and unique coin that is independent of any other payment gateway. However, using MagiPay is not entirely free and customers have to pay  transaction costs.

What exactly is the latest Seth,Sether project?

Although the market value and price of the sether token is not as satisfying as XMG, but it is beginning to become hot in 2018, what is Seth and sether, and what does it do?Sether is a smart one, it provides a completely transparent environment for carrying out marketing activities, and allows the users to advance activities, organizes work and conduct payment, use sether intelligent contract to guarantee some old technology of automation, build a new trust between brands and consumers through the performance-driven payment system.


Seth is sether's token, but there are a lot of people who look at him and think it's seth iO. Now, Seth has a wide range of applications, but if you understand that it's only in Seth english, the information we get is not comprehensive. Seth's role in the sether project, though important, is less likely to be used to pay for this business activity, so it's sether that works.

   So far, I think magi and sether have great potential, but there is no ripple effect on the payment system. The magi project is not yet mature, and sether is only indirectly promoting the development of digital currency in payment. I think magi would be more persuasive to be the preferred payment option if you had to choose between the two new projects.



根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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