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A study on the Global Machine Learning as a Service market is also beneficial and used for the estimation of the several aspects of the market which are likely to have an impact on the growth and the forecast of the market in the estimated forecast period.The report also covers the detailed analysis of the vendors and the technologies which are being used by the manufacturers for the growth of the market in the estimated forecast period. It also covers and measures the patterns of the consumers, which is likely to have an impact on the growth of the market for the estimated forecast period. It also determines and estimates the views and opinions which are expressed by the consumers.

This study covers following key players:AmazonOracle CorporationIBMMicrosoft CorporationGoogle Inc.Salesforce.ComTencentAlibabaUCloudBaiduRackspaceSAP AGCentury Link Inc.CSC (Computer Science Corporation)HerokuClustrixXeround

Request a sample of this report @ https://www.orbismarketreports.com/sample-request/81211?utm_source=Pooja

These are also used for the estimation of the strategies of the new entrants in the market. The strengths and the political factors, which are likely to affect the market is also covered in detail for the estimation of the market in the estimated forecast. The study is based on the estimation of the trends, which are based on the present, future and the strategies which are used in the past. These are used for the prediction and analysis of the market for the estimated forecast period.

The study also provides detailed analysis of the market, which consists of the growth of the regions, which is one of the major aspects which is likely to have an impact on the market. Market research is one of the methods for the determination and estimation of the growth of the global Machine Learning as a Service market in the estimated forecast period. A detailed study on the global Machine Learning as a Service market is used for the understanding the strategies, which is used by the manufacturers for increased in changes for the growth of the market in the estimated forecast period.

Access Complete Report @ https://www.orbismarketreports.com/global-machine-learning-as-a-service-market-growth-analysis-by-trends-and-forecast-2019-2025?utm_source=Pooja

Market segment by Type, the product can be split into Private cloudsPublic cloudsHybrid cloud

Market segment by Application, split into PersonalBusiness

Moreover, increased demand for the growth of the products in the specific market is also one of the major attributes which are likely to have an impact on the growth of the market in the estimated forecast period. One of the other strategy which is widely used in the market research study is the SWOT analysis.

Some Major TOC Points:1 Report Overview2 Global Growth Trends3 Market Share by Key Players4 Breakdown Data by Type and ApplicationContinued

It also provides detailed analysis of the consumer patterns which are being used and the estimation of the end users in the forecast period for the global Machine Learning as a Service market. The global Machine Learning as a Service market provides a brief summary for the estimates and the analysis of the detailed segments for the market.

For Enquiry before buying report @ https://www.orbismarketreports.com/enquiry-before-buying/81211?utm_source=Pooja

About Us : With unfailing market gauging skills, has been excelling in curating tailored business intelligence data across industry verticals. Constantly thriving to expand our skill development, our strength lies in dedicated intellectuals with dynamic problem solving intent, ever willing to mold boundaries to scale heights in market interpretation.

Contact Us : Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155

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Global Machine Learning as a Service Market Projected to Reach USD XX.XX billion by 2025- Amazon, Oracle Corporation, IBM, Microsoft Corporation,...

At TU Dresden, Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Computer Vision offers a position as

Research Associate / Postdoc

Machine Learning for Computer Vision

(subject to personal qualification employees are remunerated according to salary group E 14 TV-L)

starting at the next possible date. The position is limited for three years with the option of an extension. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG). The position aims at obtaining further academic qualification. Balancing family and career is an important issue. The post is basically suitable for candidates seeking part-time employment. Please note this in your application.

Tasks:

Requirements:

Applications from women are particularly welcome. The same applies to people with disabilities.

Please submit your comprehensive application including the usual documents (CV, degree certificates, transcript of records, etc.) by 31.07.2020 (stamped arrival date of the university central mail service applies) preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de/ by sending it as a single PDF document to mlcv@tu-dresden.de or to: TU Dresden, Fakultt Informatik, Institut fr Knstliche Intelligenz, Professur fr Maschinelles Lernen fr Computer Vision, Herrn Prof. Dr. rer. nat. Bjrn Andres, Helmholtzstr. 10, 01069 Dresden. Please submit copies only, as your application will not be returned to you. Expenses incurred in attending interviews cannot be reimbursed.

Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website: https: //tu-dresden.de/karriere/datenschutzhinweis

Please find the german version under: https://tu-dresden.de/stellenausschreibung/7713.

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Research Associate / Postdoc - Machine Learning for Computer Vision job with TECHNISCHE UNIVERSITAT DRESDEN (TU DRESDEN) | 210323 - Times Higher...

During the coronavirus pandemic, online learning came to the forefront for students of all ages.

While grammar schools, intermediate schools and high schools wont move to the web platform, colleges across the country realize they have to offer remote classes to stay competitive.

One online learning school called Coursera.org has been specializing in online education for years.

Coursera is a world-wide online learning platform founded in 2012 by Stanford professors Andrew Ng and Daphne Koller that offers massive open online courses, specializations and degrees.

Its not just another of the worlds run-of-the-mill online colleges. You will specialize in a particular field of your choice and once you graduate you will be ready to take on the world.

Right now, you can sign up for free and see what Coursera has to offer.

Coursera works with universities and other organizations to offer online courses, specializations, and degrees in a variety of subjects, such as engineering, data science, machine learning, mathematics, business, computer science, digital marketing, humanities, medicine, biology, social sciences and others.

And you can get a certified degree in a lot less time it takes at traditional colleges and universities.

According to Wikipedia, courses last approximately 4 to 10 weeks, with one to two hours of video lectures a week.

These courses provide quizzes, weekly exercises, peer-graded assignments, and sometimes a final project or exam. Courses are also provided on-demand, in which case users can take their time in completing the course with all of the material available at once. As of May 2015, Coursera offered 104 on-demand courses.

As of 2017, Coursera offered full masters degrees.

The cost you might ask? Well, you wont have to break the bank, compared to some traditional colleges and universities.

Coursera offers some free courses, but the cost of individual courses which last 4 to 6 weeks range in price from $29 to $99.

Specialized programs, which can last 4-6 months, are $39-$79 per month.

An online degree, which can take 1-3 years can range from $15,000 to $25,000, a steep discount from what private colleges and universities charge.

Click here to register now for free and explore all Coursera has to offer.

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Online learning is in and Coursera has been doing it for years with affordable college degrees - SILive.com

Companies across industries are exploring and implementingartificial intelligence(AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According toMcKinsey, embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth. But with that promise comes challenges.

Computers and machines dont come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

There are multiple ways to achieve AI, and existential to them all is data. Withoutquality data, artificial intelligence is a pipedream. There are two ways data can be manipulatedeither through rules or machine learningto achieve AI, and some best practices to help you choose between the two methods.

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems asrules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of If statements (if A, then do B, else if X, then do Y).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules wont change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what theyve been written to do.

While a rules-based system could be considered as having fixed intelligence, in contrast, amachine learning systemis adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that arent working anymore.

In practice, there are several ways a machine can learn, butsupervised trainingwhen the machine is given data to train onis generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve.What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a companys AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

This article was originally published byElana Krasner on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published June 13, 2020 13:00 UTC

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The key differences between rule-based AI and machine learning - The Next Web

Skoltech scientists and their industry colleagues have found a way to use machine learning to accurately predict rock thermal conductivity. Credit: Pavel Odinev / Skoltech

Skoltech scientists and their industry colleagues have found a way to use machine learning to accurately predict rock thermal conductivity, a crucial parameter for enhanced oil recovery. The research, supported by Lukoil-Engineering LLC, was published in the Geophysical Journal International.

Rock thermal conductivity, or its ability to conduct heat, is key to both modeling a petroleum basin and designing enhanced oil recovery (EOR) methods, the so-called tertiary recovery that allows an oil field operator to extract significantly more crude oil than using basic methods. A common EOR method is thermal injection, where oil in the formation is heated by various means such as steam, and this method requires extensive knowledge of heat transfer processes within a reservoir.

For this, one would need to measure rock thermal conductivity directly in situ, but this has turned out to be a daunting task that has not yet produced satisfactory results usable in practice. So scientists and practitioners turned to indirect methods, which infer rock thermal conductivity from well-logging data that provides a high-resolution picture of vertical variations in rock physical properties.

Today, three core problems rule out any chance of measuring thermal conductivity directly within non-coring intervals. It is, firstly, the time required for measurements: petroleum engineers cannot let you put the well on hold for a long time, as it is economically unreasonable. Secondly, induced convection of drilling fluid drastically affects the results of measurements. And finally, there is the unstable shape of boreholes, which has to do with some technical aspects of measurements, Skoltech Ph.D. student and the papers first author Yury Meshalkin says.

Known well-log based methods can use regression equations or theoretical modeling, and both have their drawbacks having to do with data availability and nonlinearity in rock properties. Meshalkin and his colleagues pitted seven machine learning algorithms against each other in the race to reconstruct thermal conductivity from well-logging data as accurately as possible. They also chose a Lichtenecker-Asaads theoretical model as a benchmark for this comparison.

Using real well-log data from a heavy oil field located in the Timan-Pechora Basin in northern Russia, researchers found that, among the seven machine-learning algorithms and basic multiple linear regression, Random Forest provided the most accurate well-log based predictions of rock thermal conductivity, even beating the theoretical model.

If we look at todays practical needs and existing solutions, I would say that our best machine learning-based result is very accurate. It is difficult to give some qualitative assessment as the situation can vary and is constrained to certain oil fields. But I believe that oil producers can use such indirect predictions of rock thermal conductivity in their EOR design, Meshalkin notes.

Scientists believe that machine-learning algorithms are a promising framework for fast and effective predictions of rock thermal conductivity. These methods are more straightforward and robust and require no extra parameters outside common well-log data. Thus, they can radically enhance the results of geothermal investigations, basin and petroleum system modelling and optimization of thermal EOR methods, the paper concludes.

Reference: Robust well-log based determination of rock thermal conductivity through machine learning by Yury Meshalkin, Anuar Shakirov, Evgeniy Popov, Dmitry Koroteev and Irina Gurbatova, 5 May 2020, Geophysical Journal International.DOI: 10.1093/gji/ggaa209

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Using Machine Learning to Accurately Predict Rock Thermal Conductivity for Enhanced Oil Production - SciTechDaily