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GTC On-Demand

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Abstract:
Designed for the business leader, this session is a getting started primer for deep learning in the enterprise. Through cross-industry use cases, panelists will discuss adoption considerations, developing teams, building proof-of-concepts, and measurement.
Designed for the business leader, this session is a getting started primer for deep learning in the enterprise. Through cross-industry use cases, panelists will discuss adoption considerations, developing teams, building proof-of-concepts, and measurement.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9937
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Abstract:
Regular expressions are as old as computing itself. Our deep learning-based approaches aim to retire this tool from the modern data scientist's tool bag. The regular expression is often introduced to computer scientists as part of their early college education, often in their first discrete structures course. In this context, they are an incredible tool used to describe languages, grammars, and syntax. In practice though, developers all over the world use them to detect data types or parse certain structures. Even for common use cases such as email or phone validation, regular expressions that capture the full breadth of cases can become untenably large. We show how neural networks can learn approximation of regular expressions so that modern data scientists and developers never have to write one again.
Regular expressions are as old as computing itself. Our deep learning-based approaches aim to retire this tool from the modern data scientist's tool bag. The regular expression is often introduced to computer scientists as part of their early college education, often in their first discrete structures course. In this context, they are an incredible tool used to describe languages, grammars, and syntax. In practice though, developers all over the world use them to detect data types or parse certain structures. Even for common use cases such as email or phone validation, regular expressions that capture the full breadth of cases can become untenably large. We show how neural networks can learn approximation of regular expressions so that modern data scientists and developers never have to write one again.  Back
 
Topics:
Accelerated Analytics, AI Startup, Deep Learning and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7515
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