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Published By: Unit4     Published Date: Apr 12, 2019
Looking for a smarter way of financial planning and budgeting? An integrated financial planning tool makes the annual budgeting process more effective and efficient, and with one centralized source of truth it is always up-to-date. Learn how Unit4 Prevero can transform your budgeting and forecasting process: from integrating operational budgets, automating key processes, supporting multiple scenario simulations, through to using artificial intelligence and deep machine learning to improve accuracy of data and power your decision making.
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integrated financial planning, unit4 prevero, corporate performance management, cpm, financial planning, corporate budgeting, forecasting, artificial intelligence
    
Unit4
Published By: Unit4     Published Date: Apr 12, 2019
Looking to improve accuracy of enterprise budgeting and financial planning? Just 1% of firms can forecast costs at 90% accuracy 30 days out. This white paper by BPM Partners and sponsored by Unit4 shows how Artificial Intelligence (AI) makes a real impact on budgeting, planning, and analysis, and when complemented with deep machine learning, gives greater accuracy to data, budget rules and budget drivers. Download now and learn how AI can help auto-tune your FP&A.
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corporate performance management, cpm, corporate budgeting, forecasting, financial planning and analysis, artificial intelligence
    
Unit4
Published By: Intel     Published Date: Apr 15, 2019
There’s never been a more urgent need for comprehensive security and surveillance solutions. GeoVision Inc. has built its business on helping meet this need, providing digital and networked video surveillance solutions to customers in 110 countries. To succeed in its highly competitive and fast-changing industry, GeoVision must always be on the lookout for ways to give its customers leading-edge performance. Find out how GeoVision is working closely with Intel to maximize the performance of the hardware using the tools in Intel® System Studio, a comprehensive development tool suite to optimize the computer vision and deep learning workloads.
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Intel
Published By: Intel     Published Date: Apr 15, 2019
o The growing video surveillance market is driving demand for advanced video analysis technologies. Businesses and organizations from all vertical sectors are looking to leverage the benefits of enhanced detection accuracy and flexibility provided by deep learning to solve their security, safety, and operations challenges. Discover how Agent Vi’s innoVi* leverages cutting-edge deep learning technology to transform the hundreds, or even thousands, of cameras deployed across a city into smart video devices, contributing to the city’s ability to improve security, safety, and incident response citywide.
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Intel
Published By: Pure Storage     Published Date: Jan 12, 2018
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions. Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.
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reporting, artificial intelligence, insights, organization, institution, recognition
    
Pure Storage
Published By: Pure Storage     Published Date: Jan 12, 2018
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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data, deep learning, automated, intelligence, pure storage
    
Pure Storage
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: Intel     Published Date: Dec 13, 2018
Technology plays a key role in online shopping, where online retailers gain a greater understanding of their customers through data from their browsing and purchasing habits. Today, when consumers shop in brick-and-mortar stores, they expect the same personalized and responsive service. To help retailers achieve this level of service, a combination of hardware and software—Intel® Vision Accelerator Design products, cameras, AI deep learning video analysis technology— do the work for you. Uncover how Advantech system uses the Intel Vision Accelerator Design with Intel Movidius VPU to drive • Overall store performance such as the number of visitors and transactions, point-of-sale data, sales per shopper and the store’s ranking, and can distinguish traffic patterns by weather and time of day • Traffic and sales analysis for better staff allocation and marketing-event planning • Store heatmap analysis for more precise merchandise placement and product promotion
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Intel
Published By: Intel     Published Date: Dec 13, 2018
Advanced image analysis and computer vision are key components of today’s AI revolution and is becoming critical for a wide range of industry applications, including healthcare, where this technology is being used to detect anomalies and improve patient care. Due to a lack of integrated tools and experience with these cutting-edge technologies, however, deploying complete systems is difficult. Applications that utilize deep learning approaches often require large amounts of highly parallel compute power, storage, and networking capabilities, along with performance optimizations for faster data analysis. The Intel and QNAP/IEI solution combines all these elements in one complete system for scalable data management for hospitals and clinics of all sizes. Read more on Intel’s and QNAP/IEI’s real-world use case on macular degeneration analysis through high-performance computing, vision capabilities, storage, and networking in a single solution.
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Intel
Published By: Pure Storage     Published Date: Apr 18, 2018
Deep learning opens up new worlds of possibility in artifi cial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Pure Storage     Published Date: Apr 18, 2018
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning- especially deep neutral networks- make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject but also helps you get started building efficient deep learning networks.
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Pure Storage
Published By: NetApp     Published Date: May 29, 2018
This white paper discusses AI infrastructure challenges and how NetApp can help you build a data pipeline for your deep learning workflows today, while future-proofing investments in your AI infrastructure. Careful infrastructure planning can smooth the flow of data through your deep learning pipeline, lead to faster time to deployment, and maximize competitive differentiation.
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NetApp
Published By: IBM APAC     Published Date: May 14, 2019
IBM PowerAI Enterprise helps to make deep learning easier and faster for organizations by bringing together some of the most popular open source frameworks for deep learning, with development and management tools in a single installable package. Designed to simplify end-toend deep learning, PowerAI Enterprise allows enterprises to spend less time on data preparation, implementation and integration, and more time training neural networks for results. IBM PowerAI Enterprise version 1.1.2 includes the most popular deep learning frameworks in one installation: - BVLC Caffe - IBMCaffe - TensorFlow - PyTorch - Keras (tensorflow-keras)
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IBM APAC
Published By: Pure Storage     Published Date: Oct 09, 2018
Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data.
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Pure Storage
Published By: Pure Storage     Published Date: Apr 10, 2019
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Pure Storage     Published Date: Apr 10, 2019
This document describes the technical reasons for and benefits of an end-to-end training system and why the Pure Storage® FlashBlade™ product is an essential platform. 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 Pure FlashBlade, a scale out, high performance, dynamic data hub for the entire AI data pipeline.
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Pure Storage
Published By: IBM     Published Date: Jul 02, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy. IDC believes that this emerging environment is to date still highly undefined, even as businesses must make critical decisions. Should businesses develop in-house or use VARs, systems integrators, or consultants? Should they deploy on-premise, in the cloud, or in some hybrid form? Can they use existing infrastructure, or do AI applications and deep learning require new servers with new capabilities? We believe that many of these questions can be
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IBM
Published By: Group M_IBM Q418     Published Date: Dec 18, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy.
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Group M_IBM Q418
Published By: Group M_IBM Q119     Published Date: Dec 18, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy.
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Group M_IBM Q119
Published By: IBM     Published Date: Jun 25, 2018
Recognizing the shift to a subscription business model required real-time customer support, Autodesk turned to IBM technology to enhance its customer experience. Using Watson Assistant, Autodesk developed a virtual agent to interact with customers, applying natural language processing (NLP) and deep learning techniques to recognize and extract the intent, context and meaning behind inquiries. Quickly resolving easy customer concerns, Watson Assistant is supporting 100,000 conversations per month, with response times 99% faster than before and leading to a 10-point increase in customer satisfaction levels for Autodesk. Find out how Watson Assistant can accelerate your customer support experience. Click here to find out more about how embedding IBM technologies can accelerate your solutions’ time to market.
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IBM
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: SAS     Published Date: May 24, 2018
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. You’ll then be ready to experiment with these methods in SAS Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
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SAS
Published By: Intel Corp.     Published Date: Nov 21, 2017
This whitepaper looks into IDC recently named deep learning, as one of the most important drivers of growth in the high-performance computing space.
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Intel Corp.
Published By: HP Inc.     Published Date: Jun 20, 2019
Four billion people now generate four quintillion bytes of data every day - and with the number of IoT devices set to increase to three times the global population by 2022 - volumes will only continue to rise. The challenge is processing the data. This is why machine learning, deep learning and all the other developing forms of AI must deliver the analytics toolset businesses need to compete.
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HP Inc.
Published By: IBM APAC     Published Date: Oct 16, 2018
Modern AI, HPC and analytics workloads are driving an ever-growing set of data intensive challenges that can only be met with accelerated infrastructure. Designed for the AI era and architected for the modern analytics and AI workloads, Power Systems AC922 delivers unprecedented speed for the AI Era like up to 5.6 times as much bandwidth, which results from The only architecture enabling NVLink between CPUs and GPUs a variety of next-generation I/O architectures: PCIe gen4, CAPI 2.0, OpenCAPI and NVLINK. Proven simplified deep-learning deployment and AI performance
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IBM APAC
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