State Machine Replication is More Expensive than Consensus.pdf Consensus and State Machine Replication (SMR) are generally considered to be equivalent problems. In certain system models, indeed, the two problems are computationally equivalent: any solution to the former problem leads to a solution to the latter, and vice versa. In this paper, we study the relation between consensus and SMR from a complexity perspective.

Towards Low Latency State Machine Replication for Uncivil Wide-area Networks.pdf We consider the problem of building state machines in a multi-site environment in which there is lack of trust between sites, but not within a site. This system model recognizes the fact that if a server is attacked, then there are larger issues at play than simply masking the failure of the server. We describe the design principles of a low-latency Byzantine state machine protocol, called RAM

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When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!

DSMA - The Database State Machine Approach

主成分回归代码matlab及示例R中的机器学习 这是我在机器学习期间开发的R脚本的存储库。 一些代码已从其原始Matlab实现中进行了改编并转换为R。 分类 欧几里得（euclidean_classifier） Mahalanobis（mahalanobis_classifier） 感知器（perceptron_classifier） 在线感知器（online_perceptron_classifier） Sum-Squared错误（sse_classifier） 回归 绘制数据和（regression_plot） 绘制回归决策边界（regression_boundary） 通用回归包装函数（regression_optimize） 线性回归 线性回归成本函数和梯度（lr_cost） 线性回归梯度下降（lr_gradientdescent） 逻辑回归 Logistic回归成本函数和梯度（logr_cost） 逻辑回归优化器（logr_optimize） 预测（logr_predict） Softmax回归 Softmax回归成本函数和梯度（softmax_cost） Softmax回归

数据科学研讨会 这是出版的的资料库。 它包含从头到尾完成该课程所必需的所有支持项目文件。 要求和设置 要开始使用项目文件，您需要： 设定 关于数据科学研讨会 为您提供了开始从事各种数据科学项目所需的基本技能。 本书将逐步介绍数据科学项目的基本组成部分，然后将所有部分放在一起以巩固您的知识并在现实世界中应用您的知识。 您将学到什么 探索有监督学习与无监督学习之间的主要区别 使用scikit-learn和pandas库处理和分析数据 了解关键概念，例如回归，分类和聚类 探索先进的技术来提高模型的准确性 了解如何加快添加新功能的过程 简化您的机器学习工作流程以进行生产 相关工作坊 如果您发现此存储库很有用，则可能需要查看我们的其他一些研讨会标题： 应用TensorFlow和Keras研讨会

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Abdullah Karasan - Machine Learning for Financial Risk Management with Python_ Algorithms for Modeling Risk-O'Reilly Media (2022)

利用人工智能预测心脏病死亡率 python machine learning deep learning