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teddykoker.com
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 Nesterov's 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 replaced with a simple neural network, which can be trained end-to-end. In this post, we will see how JAX, a relatively new Python library for numerical computing, can be used to implement a version of the optimizer introduced in the paper. |
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bdtechtalks.com
Gradient descent is the main technique for training machine learning and deep learning models. Read all about it. |
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research.google
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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mostafaelmasry.com
In this post you will find all of the posts and articles related to Microsoft azure services keep following me Azure Posts Road-Map All of my Azure PostsAzure Exam PreparationAzure Data Studio PostsAzure Storage Account PostsAzure VM PostsAzure SQL Database PostsAzure Cosmos DB PostsAzure DB open SourceAzure Big Data PostsAzure Synapse PostsAzure Data LakeAzure PowerShell... |
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