2 Node Neural Network
Simple Explanation of a 2-Node Neural Network Architecture Imagine a tiny brain with only 2 brain cells (neurons) that receive information and produce 2 outputs. Input Features [ x₁ ] Weights for Biases [ x₂ ] ──► Neuron 1 ─────► [ b₁ ]───┐ [ x₃ ] W₁₁, W₁₂, … │ [ x₄ ] ├──► [ Output₁ ] ← Node 1 output (after activation) . │ . │ [ xₙ ] Weights for │ Neuron 2 ─────► [ b₂ ] ─ ─┘ W₂₁, W₂₂, … └──► [ Output₂ ] ← Node 2 output (after activation) Real Example We Used (4 inputs → 2 nodes) Input (4 features) 2-Node Layer (Output) x₁ = 1.0 weight matrix W (4×2) bias Linear Activation Final Output x₂ = 0.5 ───────────────────────► + [b₁] ─────► Z = X·W + b ─────► ReLU(Z) ───► [ y...