S. Diana Hu
2 SlideShares 6 Seguidores 2 Seguindos
Personal Information
Organização/Local de trabalho
San Francisco Bay Area United States
Cargo
CTO at an AR startup
Setor
Technology / Software / Internet
Site
http://www.sdianahu.com
Sobre
I have built systems for machine learning or computer vision applications at different scales and architectures from mobile devices (iOS & Android), Amazon EC2, Hadoop, HPC, GPU, to embedded systems. I've worked on the full lifecycle of models and algorithms from idea, research, prototyping, evaluation to deployment. Specialties: Machine learning, Data mining, Computer vision, Pattern recognition, Signal processing, Cloud computing Programming : Python, Scala, C++, Java, Swift, Objective-C Data Analysis: Python's scientific stack, R, MATLAB Computer Vision: OpenCV Data Visualization: R, JavaScript (D3.js, Processing.js) Big Data: AWS (ec2, s3, emr), Hadoop (Mahout), Spark HPC: CUDA, MPI
Detalhes do contato
Marcadores
machine learning scalable elasticsearch recsys recommender systems solr data science lucene search
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Apresentações

(2)

Lucene/Solr Revolution 2015: Where Search Meets Machine Learning

Há 8 anos • 284 Visualizações

RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning Meets Search!

Há 8 anos • 855 Visualizações

Gostaram

(6)
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Putting the Magic in Data Science

Sean Taylor • Há 9 anos

Large-Scale Machine Learning with Apache Spark

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Collaborative Filtering at Spotify

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Recommendation at Netflix Scale

Justin Basilico • Há 10 anos

Recommender Systems in the Linked Data era

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Music recommendations @ MLConf 2014

Erik Bernhardsson • Há 10 anos
Personal Information
Organização/Local de trabalho
San Francisco Bay Area United States
Cargo
CTO at an AR startup
Setor
Technology / Software / Internet
Site
http://www.sdianahu.com
Sobre
I have built systems for machine learning or computer vision applications at different scales and architectures from mobile devices (iOS & Android), Amazon EC2, Hadoop, HPC, GPU, to embedded systems. I've worked on the full lifecycle of models and algorithms from idea, research, prototyping, evaluation to deployment. Specialties: Machine learning, Data mining, Computer vision, Pattern recognition, Signal processing, Cloud computing Programming : Python, Scala, C++, Java, Swift, Objective-C Data Analysis: Python's scientific stack, R, MATLAB Computer Vision: OpenCV Data Visualization: R, JavaScript (D3.js, Processing.js) Big Data: AWS (ec2, s3, emr), Hadoop (Mahout), Spark HPC: CUDA, MPI
Marcadores
machine learning scalable elasticsearch recsys recommender systems solr data science lucene search
Ver mais