Introduction to deep learning: minimum essence required to launch a research.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Science

Background:

• Deep learning is a rapidly advancing field within artificial intelligence.
• Its applications span numerous domains, attracting significant research interest.
• Understanding its core principles is crucial for effective implementation.

Purpose of the Study:

• To provide a foundational overview of deep learning.
• To detail the technical aspects and research steps involved in deep learning.
• To clarify the capabilities and limitations of deep learning techniques.

Main Methods:

• Explanation of deep learning concepts using an analogy of teaching image differentiation.
• Description of deep learning model architecture (input, hidden, output layers).
• Highlighting Convolutional Neural Networks (CNNs) for image processing tasks.
• Outlining a nine-step process for conducting deep learning research.

Main Results:

• Deep learning models, particularly CNNs, excel at feature extraction in image processing.
• The research process involves distinct stages from data preparation to result verification.
• Deep learning requires substantial data for training and is not a universal problem-solving tool.

Conclusions:

• Deep learning is a specialized technique, not a universal solution.
• Researchers must possess a comprehensive understanding of its strengths and weaknesses.
• Effective application necessitates awareness of data demands and task specificity.