Microsoft has begun out new AI-powered incident prioritization capabilities in Microsoft Defender alongside an expanded suite ...
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Mini-batch gradient descent in deep learning explained
Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...
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Linear regression using gradient descent explained simply
Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...
This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Abstract: The interrupted sampling repeater jamming (ISRJ) can create false targets that obscure real targets, leading to radar target detection failures. This study investigates the ISRJ ...
Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...
Abstract: Learning to Rank (LTR) aims to develop a ranking model from supervised data to rank a set of items using machine learning techniques. However, since the losses and ranking metrics involved ...
The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...
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