Deeplearning4J by Example

This talk originates from the archive. To the CURRENT program
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Thursday, December 6 2018
11:45 - 12:45

The dominant programming language for deep learning is Python. It has a wide variety of frameworks and data scientists love it due to its ecosystem and the workflows it allows. Yet when it comes to actually taking models to production, it is usually met with resistance, as in many enterprise environments Java is still king of the hill – and rightly so. It is the underpinning of big data infrastructure, provides better tooling for production monitoring and scales better to larger teams.

Deeplearning4J is both the name of a deep learning library for Java, but also the umbrella for a set of libraries aimed at the production usage of deep learning.
This session will take you along for the journey to create a Deeplearning4J-based model from scratch and take it into a production environment. The journey will start with the formulation of a problem that is then going to be solved during the live coding that follows. Each step and the reasoning for it will be comprehensively explained, such that you should be able to repeat the same process with your own data and problems.

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