In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

In recent years, the spike in the amount of information transmitted through communication
infrastructures has increased due to the advances in technologies such as cloud computing …

Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

Recurrent neural networks (RNNs) have been widely used for processing sequential data.
However, RNNs are commonly difficult to train due to the well-known gradient vanishing and …

Batch normalization (BN) is a technique to normalize activations in intermediate layers of
deep neural networks. Its tendency to improve accuracy and speed up training have …

Gatys et al. recently introduced a neural algorithm that renders a content image in the style
of another image, achieving so-called style transfer. However, their framework requires a …

In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

The agricultural sector remains a key contributor to the Moroccan economy, representing
about 15% of gross domestic product (GDP). Disease attacks are constant threats to …