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blog.research.google | ||
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teddykoker.com
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| | | | | Gradient-descent-based optimizers have long been used as the optimization algorithm of choice for deep learning models. Over the years, various modifications to the basic mini-batch gradient descent have been proposed, such as adding momentum or Nesterovs Accelerated Gradient (Sutskever et al., 2013), as well as the popular Adam optimizer (Kingma & Ba, 2014). The paper Learning to Learn by Gradient Descent by Gradient Descent (Andrychowicz et al., 2016) demonstrates how the optimizer itself can be replac... | |
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research.google
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| | | | | Posted by Bryan Perozzi, Research Scientist and Qi Zhu, Research Intern, Google Research Graph Neural Networks (GNNs) are powerful tools for levera... | |
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bdtechtalks.com
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| | | | | Gradient descent is the main technique for training machine learning and deep learning models. Read all about it. | |
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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. | ||
