In data science, your output can really only be as strong as the mentality of the team members you bring on board, the technology they leverage, and their ability to connect it all to a real-world application.
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Tracking sales and inventory trends from the POS data over time and analyzing the period-over-period results is critical to being able to predict consumer needs and stock accordingly.
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Many programs such as chatbots and virtual assistants are made possible through the use of natural language processing or NLP
Sourced through Scoop.it from: www.information-age.com
When you're asked to evaluate the potential of AI or ML to solve your organization's problems, you'd better understand the distinctions between the two.
Sourced through Scoop.it from: enterprisersproject.com
From image reconstruction to quantum tomography, scientists are applying machine learning to all areas of physics, as Marric Stephens discovers
Sourced through Scoop.it from: physicsworld.com
The search giant created a 10-point checklist that all company founders need to see.
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Accenture’s Applied Intelligence platform claims to provide non-data scientists with the tools and access to the data they need to rapidly assemble applications
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Strong collaboration between data scientists and subject-matter experts (SMEs) on data is essential for building an infrastructure of capturing data for rapid, accurate decisions. It’s important to understand the role of a data scientist in order to build your data science “dream team.”
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OpenAI's new family of language models achieves state-of-the-art performance on a number of NLP tasks -- and sometimes generates convincing original text.
Sourced through Scoop.it from: venturebeat.com
One thing common with these tech giants is their willingness to open source their innovations. These range from sentiment analysis to text categorization
Sourced through Scoop.it from: www.analyticsindiamag.com
