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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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pyimagesearch.com
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| | | | | In this tutorial, you will learn what gradient descent is, how gradient descent enables us to train neural networks, variations of gradient descent, including Stochastic Gradient Descent (SGD), and how SGD can be improved using momentum and Nesterov acceleration. | |
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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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nanonets.com
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| | | Automated information extraction is making business processes faster and more efficient. Graph Convolutional Networks can extract fields and values from visually rich documents better than traditional deep learning approaches like NER. | ||
