Neural Network Fundamentals
Discover how artificial neurons work together to learn complex patterns
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Machine Learning Basics
What are Neural Networks?
Neural networks are computing systems inspired by biological brains. They consist of interconnected artificial neurons that process information in layers, learning to recognize patterns through training. Just like your brain learns from experience, neural networks learn from data.
🧠 From Brain to Machine
🧬
Biological Neuron
- •Dendrites: Receive signals from other neurons
- •Cell body: Processes incoming signals
- •Axon: Sends signal to other neurons
- •Synapses: Connection strength determines signal
🤖
Artificial Neuron
- •Inputs: Receive numerical values (features)
- •Weights: Multiply inputs by learned importance
- •Sum: Add weighted inputs + bias term
- •Activation: Apply function to produce output
⚙️ How an Artificial Neuron Works
1
Receive Inputs
Each neuron receives multiple input values (x₁, x₂, x₃, ...)
Example: [0.5, 0.8, 0.3]
2
Multiply by Weights
Each input is multiplied by a weight (w₁, w₂, w₃, ...)
Example: 0.5 × 0.4 + 0.8 × 0.7 + 0.3 × 0.2
3
Sum Everything
Add all weighted inputs plus a bias term (b)
Sum = (inputs × weights) + bias = 0.82
4
Apply Activation
Pass sum through activation function for final output
Output = sigmoid(0.82) = 0.69
💡 Why Neural Networks Are Powerful
🎯
Universal Approximators
Can learn any continuous function given enough neurons and data
🔗
Hierarchical Learning
Early layers learn simple patterns, deeper layers learn complex concepts
🎨
Feature Extraction
Automatically discover relevant features without manual engineering
📈
Scalability
Performance improves with more data and computational power