algorithms

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Published By: Pure Storage     Published Date: Dec 05, 2018
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
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Pure Storage
Published By: Coyote Point Systems     Published Date: Sep 02, 2010
The idea of load balancing is well defined in the IT world: A network device accepts traffic on behalf ofa group of servers, and distributes that traffic according to load balancing algorithms and the availabilityof the services that the servers provide. From network administrators to server administrators to applicationdevelopers, this is a generally well understood concept.
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coyote point, adc, buyer's guide, web applications, server load, server hardware, networking, virtualized network, vlb advanced, load balancing, data center design and management
    
Coyote Point Systems
Published By: Monetate     Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximize brand value, while still allowing the control of an unlimited number of business guardrails that you define.
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monetate, intelligent, recommendations, business, individual, automation
    
Monetate
Published By: F5 Networks Inc     Published Date: Dec 08, 2017
The NSA’s Information Assurance Directorate left many people scratching their heads in the winter of 2015. The directive instructed those that follow its guidelines to postpone moving from RSA cryptography to elliptic curve cryptography (ECC) if they hadn’t already done so. “For those partners and vendors that have not yet made the transition to Suite B elliptic curve algorithms, we recommend not making a significant expenditure to do so at this point but instead to prepare for the upcoming quantum-resistant algorithm transition.” The timing of the announcement was curious. Many in the crypto community wondered if there had been a quantum computing breakthrough significant enough to warrant the NSA’s concern. A likely candidate for such a breakthrough came from the University of New South Wales, Australia, where researchers announced that they’d achieved quantum effects in silicon, which would be a massive jump forward for quantum computing.
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quantum computing, quantum, computing, browser encryption. encryptionm
    
F5 Networks Inc
Published By: FusionOps     Published Date: Jun 15, 2016
The supply chain generates huge volumes of data captured in ERP, CRM, demand planning and other systems. Download this whitepaper to learn how FusionOps Machine Learning can provide companies with a more accurate, granular understanding of their business by harmonizing these disparate data sources in the cloud, and applying machine learning algorithms.
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FusionOps
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Compression algorithms reduce the number of bits needed to represent a set of data—the higher the compression ratio, the more space this particular data reduction technique saves. During our OLTP test, the Unity array achieved a compression ratio of 3.2-to-1 on the database volumes, whereas the 3PAR array averaged a 1.3-to-1 ratio. In our data mart loading test, the 3PAR achieved a ratio of 1.4-to-1 on the database volumes, whereas the Unity array got 1.3 to 1.
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Dell PC Lifecycle
Published By: Akamai     Published Date: Jun 04, 2010
Predictive analytics have been used by different industries for years to solve difficult problems that range from detecting credit card fraud to determining patient risk levels for medical conditions. It combines data mining and machine-learning technologies to create statistical models based on historical data. It then uses these models to predict future events. Extracting the power from the data requires powerful algorithms behind predictive analytics.
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akamai, predictive, online advertising, tracking pixels, online shopping, in-market, site visitors, performance marketing
    
Akamai
Published By: Clustrix     Published Date: Sep 04, 2013
Find out how AdScience has been able to increase their revenue potential by five times using Clustrix to optimize bidding for their online ad broker agency. AdScience runs complicated algorithms to process bids for ad space based on click history. It's critical for AdScience to have instant access to smart data.
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adscience, revenue, technology, clustrix, algorithms, case study, best practices, networking, it management, knowledge management, enterprise applications, business technology
    
Clustrix
Published By: CA Technologies EMEA     Published Date: Sep 14, 2018
The misuse or takeover of privileged accounts constitutes the most common source of breaches today. CA Threat Analytics for PAM provides a continuous, intelligent monitoring capability that helps enterprises detect and stop hackers and malicious insiders before they cause damage. The software integrates a powerful set of user behavior analytics and machine learning algorithms with the trusted controls provided by CA Privileged Access Manager (CA PAM). The result is a solution that continuously analyzes the activity of individual users, accurately detects malicious and high-risk activities and automatically triggers mitigating controls to limit damage to the enterprise.
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CA Technologies EMEA
Published By: Pure Storage     Published Date: Jan 12, 2018
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around
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learning machines, automated intelligence, scalars, vectors, mathematics, classification, pure storage
    
Pure Storage
Published By: Monetate     Published Date: Oct 11, 2018
Monetate Intelligent Recommendations is the only solution that gives merchandisers & digital marketers the power to show contextually relevant product recommendations without burdening IT resources. Using manually curated or algorithmically-driven recommendations, marketers can easily support even the most complex product catalogs. Our solution filters recommendations based on customer attributes (e.g. shirt size), longitudinal behaviours (e.g. browsing behaviour), and situational context (e.g. product inventory at local stores). Best of all, an orchestration layer intelligently selects which algorithms and which filters to apply in any given situation, for any particular individual.
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Monetate
Published By: Monetate     Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximise brand value, while still allowing the control of an unlimited number of business guardrails defined by you.
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monetate, intelligent, recommendations, business, individual, automation
    
Monetate
Published By: IBM     Published Date: May 17, 2016
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
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ibm, big data, inline analytics, business analytics, roi
    
IBM
Published By: CA Technologies EMEA     Published Date: Aug 07, 2017
Generate rich virtual data that covers the full range of possible scenarios and provide the unconstrained access to environments needed to deliver rigorously tested applications on time and within budget. Model complex live system data and apply automated rule-learning algorithms to pay off technical debt and uncover in depth understanding of composite applications, while exposing virtual data to distributed teams on demand and avoiding testing bottlenecks.
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virtual services, data, avoid project delays, ca technologies, continuous testing, testing effort, service virtualisation, delivery ecosystem
    
CA Technologies EMEA
Published By: MobileIron     Published Date: Aug 20, 2018
MobileIron knows that cybercriminals are continuously generating more advanced ways to steal your data by any means necessary. That’s why we are committed to continually innovating and delivering new solutions that help our customers win the race against time to get ahead of the latest mobile security threats. As part of that commitment, MobileIron Threat Defense supports the five critical steps to deploying advanced, on-device mobile security. Our solution provides a single, integrated app that delivers several key advantages: • A single app of threat protection is fully integrated with EMM. • No user action is required to activate or update on-device security. • Advanced mobile security blocks known and zero-day threats across iOS and Android devices with no Internet connectivity required. • Machine-learning algorithms instantly detect and remediate on-device DNA threats.
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mobile, threat, defense, strategy, mobileiron, innovation
    
MobileIron
Published By: Amazon Web Services     Published Date: Feb 01, 2018
At Amazon, we’ve been investing deeply in AI for more than 20 years. Machine learning (ML) algorithms drive many of our internal systems, and have formed the core of our customers' experience —from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, and our new retail experience, Amazon Go. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every executive, developer, and data scientist.
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machine learning, algorithms, interal systems, amazon
    
Amazon Web Services
Published By: Reputation.com     Published Date: Feb 26, 2018
In 2017, review sources proliferated, consumers became more savvy about the validity of online reviews, and the position of Chief Experience Officer started to gain traction among locationbased organizations. ORM and SEO became increasingly intertwined as Google refined its search algorithms with a strong emphasis on reviews and star ratings.
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Reputation.com
Published By: Adobe     Published Date: Oct 24, 2018
Adobe automates the process of turning insights into action by connecting Adobe Analytics to other solutions in Adobe Experience Cloud, including Adobe Target and Adobe Audience Manager. Four features make this possible: • Anomaly detection. The technology automatically analyzes trends and determines if they are statistically significant — in milliseconds. • Analyze play button. With analytics, you can take insights and connect them to your email, DMP, and personalization platform in seconds. • Intelligent alert. A built-in alerting system sends an SMS text or email when it detects an anomaly. There are also predictive algorithms that help you forecast how often the alert is likely to trigger. You can set these to only notify you of the most important changes. • Intelligent recommendations. It’s simply impossible to manually create every alert you might need, so Adobe is building machine learning directly into analytics to analyze users’ behaviors. Like a virtual data assistant, it co
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Adobe
Published By: Reputation.com     Published Date: Jun 29, 2018
The past year ushered in some big changes for Online Reputation Management (ORM) — and the practice has become indispensable for any location-based enterprise. In 2017, review sources proliferated, consumers became more savvy about the validity of online reviews, and the position of Chief Experience Officer started to gain traction among locationbased organizations. ORM and SEO became increasingly intertwined as Google refined its search algorithms with a strong emphasis on reviews and star ratings. This year, expect to see these four trends move to the forefront: 1) Google will extend its dominance in online review volume and consumer exposure, eclipsing all other specialty sites. 2) SEO will be reinvented as user-generated reviews weigh more heavily in search rankings. 3) The voice of the customer will no longer be siloed across disconnected categories. 4) Consumer feedback from reviews and social media will drive operational improvements.
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Reputation.com
Published By: FICO     Published Date: Nov 10, 2015
"ACG Michigan, a large auto insurance underwriter in the US state of Michigan, needed a user-friendly system that would enable its agents (internal and independent) to churn out precise and consistent policy quotes and underwriting decisions. They turned to FICO Blaze Advisor decision rules management system to create an enterprise decision management framework to execute decisions. Learn more on how FICO Blaze Advisor helped ACG Michigan automate its underwriting About FICO FICO (NYSE: FICO), formerly known as Fair Isaac, is a leading analytics software company, helping businesses in 90+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company's groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimiz
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decision analytics, optimization, profitability
    
FICO
Published By: Marketo     Published Date: Mar 22, 2018
Advertising on social media platforms has changed tremendously. Recent updates to many social networks' algorithms give users a better experience—one with less promotional content and more relevant content that they want to see. This means that, as a marketer, you need to supplement your organic posts with paid promotion to get your posts seen by your audience. Download this eBook to learn more!
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Marketo
Published By: ESV Digital     Published Date: Feb 10, 2015
The multiplication of marketing channels and devices concerning consumers has greatly increased the complexity faced by brands in their marketing efforts. This white paper aims to explain the issues, the principal attribution models used and the related challenges.
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esv digital, algorithms, interactions, point of contact, nurturing, marketing data, marketing analytics, business technology
    
ESV Digital
Published By: SAS     Published Date: Mar 06, 2018
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated drive as you read the morning news. We are getting very close to that kind of scenario, and companies like Ford expect to have production vehicles in the latter part of 2020. Driverless cars are just one popular example of machine learning. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments. The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems. Other reasons for the increased momentum include: maturing capabilities with methods and algorithms refactored to run in memory; the
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Machines learn by studying data to detect patterns or by applying known rules to: • Categorize or catalog like people or things • Predict likely outcomes or actions based on identified patterns • Identify hitherto unknown patterns and relationships • Detect anomalous or unexpected behaviors The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
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SAS
Published By: IBM     Published Date: Apr 03, 2017
Predictive analytics is powerful. It can help drive significant improvement to an organization’s bottom line. Look for ways to use it to grow revenue, shrink costs and improve margins. Provide a platform that enables your data scientists to work efficiently using tools and algorithms they prefer. Enhance your analyses with internal and external data, structured and unstructured data. Then make the analytics accessible in order to reap the full benefits of these valuable analyses. Stay ahead of the curve in your market with predictive analytics, and give your organization a competitive advantage and an improved bottom line.
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predictive analytics, analytics, data analytics, financial marketing, market analytics, data resources
    
IBM
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