… Deep learning paradigm uses a massive ground truth designated data to find the unique
features, combinations of features and then constructs an integrated feature extraction and …

… to create deep learning environments. In this chapter, I will analyze the main characteristics
of the Learning paradigm, … every aspect of the deep learning process in our own classrooms. …

… In this paper we propose a novel strategy based on deep learning: differently from other
methods, a first level feature vector is extracted by using a set of features derived from depth …

… in SOA approaches based on the deep learning architecture. A comparison of the previous
… the shift in SOA towards the new paradigm of the deep learning architecture. The rest of the …

… , this study presents a Deep Learning (DL)-based paradigm that computes nearly seven …
the cross-validation (training and testing) paradigm. The DL architecture consists of cascaded …

… The most recent breakthroughs in deep learning for text classification often center on …
Here we look at how lexicons, machine learning, and deep learning may all be used to better …

… The amazing success of deep learning in various fields, particularly in computer science,
has recently stimulated increasing interest in applying it to address those challenges. Hence, in …

… Although the deep learning paradigm emphasizes on … , the advancement of deep learning
optimization algorithms can also … in order to enhance deep learning using other mathematical …

… the most successful implementations of the deep learning (DL) … deep learning architectures
has emerged as the first attempted technologies to address AI challenges (4). Deep learning …

… deeper manner, the neural network under ML approach promotes a significant role. In-specific,
deep learning … and machine learning (AI/ML) paradigm that mimics how people learn. So, …