ðĪ ML Models
ML - Neural Network Basics
Function approximators built from layers of connected nodes (neurons). Each neuron does three things: multiply inputs by weights, add a bias, apply an activation function. Stack layers for hierarchical feature learning â early layers find simple patterns, later layers compose them into complex ones.
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XP
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How a Single Neuron Works
inputs: [x1, x2, x3] weights: [w1, w2, w3] bias: b output = activation(w1*x1 + w2*x2 + w3*x3 + b)
That's it. A neuron is just a weighted sum passed through a nonlinear function. The power comes from combining thousands of these.