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If successful, DeepMind's goal to bridge deep learning and classical computer science could revolutionize AI and software as we know them.
Last week, we built our Deep Learning foundation, learning about perceptrons and the backprop algorithm. This week, we are learning about optimization methods. We will start with Stochastic Gradient ...
Recently, Shaila Niazi, a third-year doctoral student in Çamsari’s lab, achieved a significant breakthrough in that effort, becoming the first to use probabilistic hardware to train a deep generative ...
Deep learning algorithm used to pinpoint potential disease-causing variants in non-coding regions of the human genome The methods help identify 'footprints' that indicate binding sites and reveal ...
Researchers have developed and validated a deep learning algorithm that can identify and outline ('segment') a non-small cell lung cancer (NSCLC) tumor on a computed tomography (CT) scan within ...
The algorithm, however, has some limitations including a large memory requirement, limited context awareness within large tissue slides and the fact that it is limited to a single imaging modality.
Researchers at the University of Tokyo developed ADOPT, a novel optimization algorithm that overcomes convergence issues in adaptive gradient methods, promising more reliable and efficient ...
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