Sep 7, 2023 · Some of the critical topics in deep learning, namely, transfer, federated, and online learning models, are explored and discussed in detail. Finally, challenges and future directions are outlined to provide wider outlooks for future researchers. Discover the latest articles, news and stories from top researchers in related subjects.

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled.

In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used.

Nov 27, 2024 · Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics.

Mar 31, 2021 · In this paper, an overview of DL is presented that adopts various perspectives such as the main concepts, architectures, challenges, applications, computational tools and evolution matrix. Convolutional neural network (CNN) is one of the most popular and used of DL networks [19, 20].

Aug 22, 2023 · This research paper delves into the realm of AI algorithms, deep learning, and neural networks, dissecting their advancements and multifaceted applications.

Apr 25, 2023 · In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches.

Deep learning, an innovative paradigm within artificial intelligence, brings about significant transformations across diverse sectors, ranging from healthcare to autonomous systems. This work explores the latest developments and uses of Deep Learning Algorithms.

Apr 22, 2019 · We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement …

Deep learning is a subset of machine learning that employs multi-layered neural networks to analyze various data factors. These algorithms automatically learn representations from data, allowing them to perform tasks like image recognition, natural language processing, and even mastering complex games like chess or Go.