… Over the last decade, deep learning (DL) methods have been extremely successful and
widely used to develop artificial intelligence (AI) in almost every domain, especially after it …

… Intelligence (AI) systems to alternative finance audience, and other nonexperts in the field of
AI. Deep Learning … used by deep learning systems in decision-making. AI, and in particular …

… A central tenet of the symbolic paradigm is that intelligence results from … deep learning is
to develop architectures capable of discovering objects and relations in raw data, and learning …

… wall plaque segmentation used artificial intelligence (AI) methods on … AI” paradigm where
training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) …

… Deep learning paradigm uses a massive ground truth designated data to find the unique …
neural network, which can learn and take intelligent decisions on its own, whereas machine …

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

… them to adopt artificial intelligent techniques, ie, deep learning, to manage … deep learning
to inexpensively shift the computing needs from rule-based route computation to deep learning …

… This simple paradigm is at the core of much larger and more sophisticated neural network
architectures today, but the jump from perceptrons to deep learning was not a smooth one. …

… The origin of deep learning goes back to the birth of artificial intelligence in the 1950s,
when there were two competing visions for how to create an AI: one vision was based on logic …

… intelligence services. Yet, the compression, storage, and communication of the deep learning
… This paper presents the deep learning model communication paradigm based on multiple …