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✓Save more than 500 € and get ML Intro Day for free
✓ Workshop day for free
✓10 % Team Discount
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Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
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Register until November 7th:
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Waren Long
source{d}
Waren is working at source{d} as a research software engineer in the Machine Learning team. After graduating from Paris in Optimization theory, he joined the Department of Mathematics at UCLA designing optimal networks to minimize the cost of traffic congestion. Today, Waren is applying NLP techniques to model source code and predict program properties. More recently he’s been developing ML-based solutions to gain engineering visibility into a codebase and its processes.
This Speaker belongs to the Dieser Speaker gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Online Edition Online Edition .
This Speaker belongs to the Dieser Speaker gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Munich Munich .
This Speaker belongs to the Dieser Speaker gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .
All talks by Waren Long
No current talks available.
All talks by Waren Long from other editions
Berlin 2019 Berlin 2019 -How to Identify Collaborators in large Codebases using Unsupervised Learning
All talks by Waren Long
No current talks available.
All talks by Waren Long from other editions
Berlin 2019 Berlin 2019 -How to Identify Collaborators in large Codebases using Unsupervised Learning
All talks by Waren Long
No current talks available.
All talks by Waren Long from other editions
Berlin 2019 Berlin 2019 -How to Identify Collaborators in large Codebases using Unsupervised Learning
All talks by Waren Long
Berlin 2019 Berlin 2019 -How to Identify Collaborators in large Codebases using Unsupervised Learning
All talks by Waren Long
Berlin 2019 Berlin 2019 -How to Identify Collaborators in large Codebases using Unsupervised Learning