Big data analytics und machine learning die Herrschaft der Daten

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This document discusses big data, analytics, and machine learning. It provides examples of companies utilizing these technologies, such as Google, Amazon, and Facebook. It also summarizes different machine learning algorithms like linear regression, neural networks, and deep learning. Key concepts covered include statistics, data mining, supervised versus unsupervised learning, and different applications of machine learning.

Data & Analytics

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KB

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©Google,Amazon,Facebook,Apple

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©Microsoft,SAP,GE,Siemens

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©Intel,1971

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Algorithmus EntscheidungDaten
©PeterSeeberg/Softing,2016

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In 1959, Arthur Samuel
defined machine learning
as a "Field of study that
gives computers the ability
to learn without being
explicitly programmed"

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What’s the difference between
machine learning, statistics,
and data mining?
An expert opinion:
There is no difference

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statistics……………

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data mining….
statistics……………

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machine learning
data mining….
statistics……………

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statistics……………
data mining….
machine learning

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©AndrewNg,2016

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Alter
Sorte
alt jung
veredelt natürlich
reichhaltig mager
ja
ja nein
nein
Boden

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Lineare
Regression

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Deep Learning
Neural Network
©MichaelNielsen/Jan2016

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©AndrewNg,2016

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©USUSoftwareAG,2016

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©Microsoft,2016
PrincipalComponentsAnalysis

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SelfOrganizingMap(SOM)

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Support Vector ClusteringNeural Networks
Principal Component
Analysis
K-Means
K-Nearest
Support Vector Machines
Algorithms for Anomaly Detection
Supervised / Classification Unsupervised / Clustering
Kalman Filter

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„Mittlerweile
könne
man
dank
technischer
Anwendungen
wie
Skype
die
richtigen
Übersetzer
zuschalten.“
©Google,2016
Reinforced Learning

39Copyright © 2016 Softing Industrial. All rights reserved.
„Mittlerweile
könne
man
dank
technischer
Anwendungen
wie
Skype
die
richtigen
Übersetzer
zuschalten.“

40Copyright © 2016 Softing Industrial. All rights reserved.
„Mittlerweile
könne
man
dank
technischer
Anwendungen
wie
Skype
die
richtigen
Übersetzer
zuschalten.“

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©TechnischenUniversitätDelft(TUDelft),Microsoft,
INGBank,MuseumHetRembrandthuisundMaurithuis,2016

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©Microsoft,2016

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60%
60%
40%
40%

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©Microsoft,2015

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©Apple,2015

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©Google,2015

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©Microsoft,http://how-old.net/#

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©Microsofthttps://www.microsoft.com/cognitive-services/en-
us/emotion-api

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azure
©Microsoft,2015

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sap
©Microsoft,2015

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sap
©SAP,2015

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google
©Google,2015

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©Heise,2016

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©IBM,2015

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©Google,2016

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©TwentiethCenturyFox,2004

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©ZoharLazar,NewYorkTimes,2016

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ElonMusk
StephenHawkingSteveWozniak
BillGates

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JürgenSchmidhuber
RaymondKurzweil

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Algorithmus EntscheidungDaten
©PeterSeeberg/Softing,2016

66Copyright © 2016 Softing Industrial. All rights reserved.
Peter Seeberg
Business Development Manager
Industrial Data Intelligence
Softing Industrial Automation GmbH
Vielen Dank für Ihre Aufmerksamkeit!
www.industrial.softing.com

Industrial Edge Analytics providing Real-time, On-Site Insight Ruling data and the resulting sequence change from “algorithm -> data -> decision” towards “data -> algorithm -> decision“ represents the actual revolution taking place in front of our eyes. Industrie 4.0 may eventually result in an autonomous economy in which algorithms communicate with each other for the higher well-being of man. Before we get there algorithms can help improve Overall Equipment Effectiveness (OEE), representing machine availability and performance as well as quality of produced goods. The goal – improving OEE – is not new. New is the data-based approach by means of machine learning algorithms. Despite numerous endeavors from providers and organizations, most decision makers have a bad feeling when suggested to move their production data into the cloud. By means of an „edge“ solution close to or part of field devices and machines, data stays in the production line and is processed on the spot. Additional security precautions are unnecessary.

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Peter Seeberg

OSGi Alliance presentation at CeBIT IoT Summit from March 2016. Presented by Dr Richard Nicholson. From the Environment to Manufacturing, from the Consumer to Education, from Finance to Government, IoT has the potential to transform markets and businesses and almost everything we know today. However, to achieve this change, one must first be cognizant of, and then address, the business and engineering challenges posed by IoT. Business as usual in IT will not deliver the promises of IoT! This talk will review the fundamental characteristics required for any pervasive IoT solution to achieve this transformation and discuss the central importance of an industry standard for software modularity. These will be compared and contrasted against some of the hot IT trends of the last decade. The presentation will conclude with an overview of the OSGi Alliance and its activities within the IoT, along with industry examples and some opportunities for you to take advantage or and get involved with OSGi. http://www.cebit.de/event/business-iot-economic-alchemy-or-another-anti-climax/KEY/70812

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一比一原版(CBU毕业证)卡普顿大学毕业证如何办理

ahzuo

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Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Subhajit Sahu

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...

Timothy Spann

一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理

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Everything you wanted to know about LIHTC

Roger Valdez

一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理

oz8q3jxlp

原版定制【微信:41543339】【(Deakin毕业证书)迪肯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Analysis insight about a Flyball dog competition team's performance

roli9797

Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...

John Andrews

SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation" Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults Description: Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project. Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas

Machine learning and optimization techniques for electrical drives.pptx

balafet

The affect of service quality and online reviews on customer loyalty in the E...

jerlynmaetalle

办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样

apvysm8

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一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理

74nqk8xf

毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake

Walaa Eldin Moustafa

Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines. #SQL #Views #Privacy #Compliance #DataLake

做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样

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Nanandann Nilekani's ppt On India's .pdf

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Learn SQL from basic queries to Advance queries

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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively. Key Highlights: Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation. Advanced Queries: Learn to craft complex queries to uncover deep insights from your data. Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets. Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios. Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making. Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data! #DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics

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Big data analytics und machine learning die Herrschaft der Daten

1. Big Data, Analytics und Machine Learning: die Herrschaft der Daten München 18. Juli 2016 Tag der Technik Peter Seeberg, Albert Krohn

2. 2Copyright © 2016 Softing Industrial. All rights reserved. 1 0 1 1 0 11 11 0 0 0 0 10 0 0 1 0 0 0 1 11 11 1 1 1 1 1 1 1 1 1 1 11 10 1 0 1 1 1 10 0 000 0 0 1 1 1 0 0 0 00 1 111 1 1 1 1 10 00 0 0 0 0 0 000 0 0 0 0 0 1 1 1 1 1 1 1 1 0 00 0 00 0 0 0 0 0 0 0 0 11 1 1 1 1 111 1 1 1 00 0 0 0 0 0 0 0 0 01 11 11 1 1 1 11 1 1 10 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 00 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 11 1 11 1 1 1 111 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 0 000 0 00 0 0 0 0 0 0 0 0 000 00 0 0 000 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 11 1 1 1 1 11 1 1 1 1 111 1 1 111 11 1 11 11 1 1 1 1 1

10. 10Copyright © 2016 Softing Industrial. All rights reserved. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed"

37. 37Copyright © 2016 Softing Industrial. All rights reserved. Support Vector ClusteringNeural Networks Principal Component Analysis K-Means K-Nearest Support Vector Machines Algorithms for Anomaly Detection Supervised / Classification Unsupervised / Clustering Kalman Filter

66. 66Copyright © 2016 Softing Industrial. All rights reserved. Peter Seeberg Business Development Manager Industrial Data Intelligence Softing Industrial Automation GmbH Vielen Dank für Ihre Aufmerksamkeit! www.industrial.softing.com

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