Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

The ongoing advancements in network architecture design have led to remarkable
achievements in deep learning across various challenging computer vision tasks …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

The exponential increase in generated data as well as the advances in high-performance
computing has paved the way for the use of complex machine learning methods. Indeed, the …

This book focuses on deep learning (DL), which is an important aspect of data science, that
includes predictive modeling. DL applications are widely used in domains such as finance …

Neural Architecture Search (NAS), a promising and fast-moving research field, aims to
automate the architectural design of Deep Neural Networks (DNNs) to achieve better …

Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …

An essential point in waste management, which is a matter of great importance for the
environment and nature, is waste collection from temporary storage points. Since the …

Evolutionary Computation algorithms have been used to solve optimization problems in
relation with architectural, hyper-parameter or training configuration, forging the field known …