In business, much to the data scientist’s pleasure, so much of optimization is in finding an even narrower local maximum or minimum. That’s a key reason why deep learning systems are of such ...

With the advent of massively parallel computing coprocessors, numerical optimization for deep-learning disciplines is now possible. Complex real-time pattern recognition, for example, that can be used ...

Objective functions—mathematical formulations that define what success looks like—provide this essential clarity. While common in traditional optimization, these structured targets are often ...

ELEC_ENG 395, 495: Optimization Techniques for Machine Learning and Deep Learning. This course is not currently offered. Prerequisites A thorough understanding of Linear Algebra and Vector Calculus, ...

This technical paper titled “DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks” is co-authored from researchers at The University of Texas at Austin, Intel, ...

Microsoft has released DeepSpeed, a new deep learning optimization library for PyTorch, that is designed to reduce memory use and train models with better parallelism on existing hardware.