Lesson 1 of 8
From linear models to neural networks
Explanation
A single neuron is barely more than the linear model you already built: it takes inputs, multiplies each by a weight, adds a bias, then passes the result through an activation function (a nonlinear squashing function like sigmoid or ReLU). A neural network is many of these neurons arranged in layers, each layer feeding the next — that stacking of simple pieces is what lets networks represent very complex patterns.
Visual explanation
Read this from left to right: what goes in, what happens, and what comes out.
Extra layers transform the input in stages before making the final prediction.
In plain language
A simple linear model makes one straight-style decision. Neural networks add layers so they can learn more complicated patterns.
Remember
More layers can learn richer patterns, but need more care and data.
What to do
Why is a single linear model not enough to represent complex patterns like recognizing handwritten digits, no matter how large you make the weights?