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 ...

In addition to pure deep neural networks (DNNs), sometimes people use hybrid vision models, which combine deep learning with classical machine learning algorithms that perform specific sub-tasks.

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, ...

Also fueling deep learning's growth was the availability of huge labeled data sets for which a learning algorithm can identify the correct answer—“cat,” for example, when inspecting an image ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Deep reinforcement learning agents still need huge amounts of data (e.g., thousands of hours of gameplay in Dota and StarCraft), but they can tackle problems that were impossible to solve with ...

The brittleness of deep learning systems is largely due to machine learning models being based on the “independent and identically distributed” (i.i.d.) assumption, which supposes that real ...

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This ...