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FPGA vs. GPU: Which is better for deep learning?

IBM Big Data Hub

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Deep learning requires a tremendous amount of computing power. FPGA programming and reprogramming can potentially delay deployments.

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Digital Transformation: Exploring AI

Archives Blogs

Action 8 of the plan specifically speaks to improving data in order to support artificial intelligence (AI) research in federal agencies. Minnesota Mining and Manufacturing) Plant Showing an Employee Working on one of the Products. Have you seen the administration’s 2020 Federal Data Strategy ? Interior of the 3M Co.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. What is machine learning?

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Data monetization: driving the new competitive edge in retail

CGI

Retailers have the opportunity to learn from the expertise of organizations that have built much of their success on data mining. Personalizing the omni-channel customer journey using artificial intelligence is another area where marketers can benefit from the experience of front-runners. Achieving next-level personalization.

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Artificial Intelligence: 6 Step Solution Decomposition Process

Bill Schmarzo - Dell EMC

The conversation is simple because the objective is simple: How do I become more effective at leveraging (big) data and analytics (artificial intelligence) to power my business? Artificial Intelligence Solution Decomposition Process. Figure 1: The Evolution of AI, ML and DL (Source: Nvidia ).

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Part 1: OMG! Not another digital transformation article! Is it about understanding the business drivers?

ARMA International

This includes defining the product scope of the DT journey and the digital products and services that will deliver transformative change for a new future. Part 3 will discuss how to manage the various DT risks. One essential step is developing the DT business case and connecting it with the critical success factors (CSFs) and the product scope.