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AI & Engineering Academics›🌿 AI Sprouts›Lessons›Introduction to Neural Networks - How AI Thinks in Layers
🕸️
AI Sprouts • Beginner⏱️ 18 min read

Introduction to Neural Networks - How AI Thinks in Layers

Public lesson preview

Explore how neural networks mimic the brain, process information through layers, and learn from their mistakes.

Programme
AI Sprouts
Level
Beginner
Reading time
18 min read

Learning objectives

By the end of this lesson, you will understand:

  • ✓The Brain Analogy
  • ✓The Three Types of Layers
  • ✓Weights and Biases: The Volume Knobs
  • ✓How a Neural Network Learns
  • ✓Visual Walkthrough: Classifying a Handwritten Digit

Lesson outline

  1. The Brain Analogy
  2. The Three Types of Layers
  3. 1. Input Layer
  4. 2. Hidden Layers
  5. 3. Output Layer
  6. Weights and Biases: The Volume Knobs
  7. How a Neural Network Learns
  8. Step 1: Forward Pass
  9. Step 2: Calculate the Error
  10. Step 3: Backpropagation
  11. Step 4: Update Weights
  12. Visual Walkthrough: Classifying a Handwritten Digit
  13. Activation Functions: The Gatekeepers
  14. Why "Deep" Learning?
  15. Key Takeaways

Introductory preview

Decision trees and KNN are powerful, but the technology behind today's most impressive AI - from ChatGPT to self-driving cars - is the neural network . Inspired by the human brain, neural networks can learn incredibly complex patterns that simpler algorithms cannot. Let us peel back the layers and see how they work.

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