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Artificial Intelligence (AI)
Field focused on developing systems that mimic cognitive functions.
Narrow Intelligence
Specialized capability designed for a single task (e.g., spam filtering).
Data-Driven Learning
Approach where outcomes help generate logic:
Information + Outcomes → Logic
Conventional Coding
Rule-based method using instructions to derive outcomes:
Inputs + Rules → Outcomes
Network Models
Inspired by biology; simulate brain-like decision processes.
Perceptron
Fundamental unit with one input set and one output—no intermediates.
Layered Architecture
Composed of:
- Entry Layer (Yellow): Basic evaluations
- Intermediate Layer (Blue): Abstract reasoning
- Final Layer (Red): Concludes prediction/output
Deep Structures
Multiple intermediary stages enable intricate pattern discovery.
Used in vision systems:
- Lower Tiers → Edge detection
- Higher Tiers → Shape/character recognition
Multi-Tiered Logic
- Initial Layer (Yellow): Fundamental checks
- Secondary Layer (Blue): Aggregated evaluations
- Tertiary Layer (Green): Complex deductions
- End Layer (Red): Conclusive outcome
Hierarchical Learning
Advanced subset of data-driven learning; drives recent innovation.
Manages sophisticated operations (e.g., facial recognition).
Comparison
| Data Learning | Hierarchical Intelligence |
|---|---|
| AI subfield | Sub-area of data learning |
| Fewer samples needed | Relies on extensive input |
| Human-guided | Autonomous adjustment |
| Simple functions | Intricate pattern mapping |
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What You'll Learn:
- 📌 Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn
- 📌 What is Machine Learning? | Machine Learning Basics | Machine Learning Tutorial | Edureka