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www.exxactcorp.com | ||
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www.shaped.ai
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| | | | | In this article, we'll take a deep dive into PinSage, a state-of-the-art GCN framework developed at Pinterest for learning high-quality embeddings of nodes in massive, billion-scale graphs. Through a novel combination of techniques spanning sampling, dynamic graph construction and distributed computing, PinSage achieves order-of-magnitude speedups over existing GCN approaches while delivering substantial gains in recommendation performance. Understanding the innovations powering PinSage provides a window into the frontier of deploying deep learning on web-scale systems. | |
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blog.fastforwardlabs.com
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| | | | | Graph Neural Networks (GNNs) are neural networks that take graphs as inputs. These models operate on the relational information in data to produce insights not possible in other neural network architectures and algorithms. While there is much excitement in the deep learning community around GNNs, in industry circles, this is sometimes less so. So, I'll review a few exciting applications empowered by GNNs. Overview of Graphs and GNNs A graph (sometimes called a network) is a data structure that highlights the relationships between components in the data. | |
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keymakr.com
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| | | | | Learn about AI training methods: supervised, unsupervised, deep learning, open source models, and their deployment on edge devices. | |
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simonharlingblog.com
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| | | Is the problem you face real or imaginary? I've sat through enough studies and opinion polls to realise it's a question worth asking. Imaginary problems feel real - and that's the problem. Why are 80%* of physiotherapists podcasters men? Recognising that an imaginary problem feels real is the first step to a better conversation about | ||
