Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Deep learning (DL) is a pivotal technology in the Fourth Industrial Revolution, stemming from artificial neural networks (ANNs).
• DL's ability to learn from data has led to widespread applications in healthcare, cybersecurity, and text analytics.
• Developing effective DL models is challenging due to data variability and the 'black-box' nature of many methods.

Purpose of the Study:

• To present a structured and comprehensive overview of deep learning techniques.
• To introduce a taxonomy of DL methods, categorizing them by learning type (supervised, unsupervised, hybrid).
• To summarize key application areas and identify future research directions for DL.

Main Methods:

• A structured review of deep learning methodologies.
• Development of a taxonomy for classifying deep learning approaches based on learning paradigms.
• Summarization of current real-world applications and potential future research avenues.

Main Results:

• A classification of deep learning techniques, including discriminative, generative, and hybrid learning models.
• An overview of diverse application domains where DL is currently utilized.
• Identification of ten potential areas for future advancements in deep learning modeling.

Conclusions:

• This article offers a foundational guide to deep learning, useful for both academic and industry professionals.
• Understanding DL taxonomy and applications is crucial for advancing the field.
• Future research should focus on improving DL model interpretability and addressing real-world complexities.