In recent years, we’ve been fortunate to see a growing number of excellent machine learning tools, such as TensorFlow, PyTorch, DeepLearning4J, and CNTK for neural networks, Spark and Kubeflow for very-large-scale pipelines, and scikit-learn, ML.NET, and the recent Tribuo for a wide variety of common models. However, models are typically part of an integrated...
machine learning Articles
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- Topics:
- machine learning,
- artificial intelligence,
- Data
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Co-authors: Benjamin Le, Daniel Gmach, Aman Grover, Roshan Lal, Jerry Lin, Austin Lu, Qingyun Wan, and Leighton Zhang Feature engineering is foundational for building artificial intelligence (AI) that powers products at LinkedIn. Recently, “representation learning” or “feature learning” has started replacing manually engineered features, as they provide...
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Co-authors: Parag Agrawal, Ankan Saha, Yafei Wang, Aastha Nigam, and Eric Lawrence Figure 1: A heterogeneous social network LinkedIn’s “People You May Know” (PYMK) feature has long been used by our members to form connections with other members and expand their networks. As member networks become more heterogeneous, the People You May Know tab (MyNetwork tab...
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Our logo is inspired by the chameleon: You can enable personalization on your ranking model with GDMix, bringing a personalized...
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Co-authors: Sriram Vasudevan, Cyrus DiCiccio, and Kinjal Basu At LinkedIn, our imperative is to create economic opportunity for every...
- Topics:
- Spark,
- artificial intelligence,
- machine learning,
- Data,
- Open Source
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Co-authors: Grace Tang, Pavan K. Ganganahalli Marulappa, Montinee Khunvirojpanich, and Ting Chen LinkedIn is an active professional...