An Introductory Review of Deep Learning for Prediction Models With Big Data

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Deep learning represents a significant paradigm shift in AI and machine learning.
• Recent successes in image and speech recognition have spurred widespread interest in deep learning applications across various big data domains.
• The underlying mathematical and computational complexity of deep learning poses challenges, particularly for interdisciplinary researchers.

Purpose of the Study:

• To provide an introductory review of fundamental deep learning architectures.
• To demystify the core methodologies for a broader scientific audience.
• To equip data scientists with essential knowledge of deep learning building blocks.

Main Methods:

• Review of key deep learning models including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks.
• Explanation of their core architectural principles.
• Discussion on the flexible composition of these architectures.

Main Results:

• Identification of D-FFNN, CNNs, DBNs, AEs, and LSTM as major deep learning architectures.
• Demonstration that these core components can be combined modularly for specific applications.
• Establishment of a foundational understanding of these networks.

Conclusions:

• A basic grasp of these core deep learning architectures is essential for data scientists.
• These foundational models are versatile and can be adapted for diverse, application-specific needs.
• Understanding these networks prepares researchers for future innovations in artificial intelligence.