Build a single neuron
A neuron is the building block of a neural network, and it does two things you already know. First it takes a weighted sum of its inputs and adds a bias, exactly like the linear model. Then it passes that one number through an activation function. With sigmoid as the activation, the output is a number between 0 and 1 that can be read as how strongly the neuron says yes.
About this lesson
Words to know
- neuron
- The basic unit of a network: a weighted sum of its inputs plus a bias, passed through an activation function.
- dot product
- Multiply two lists of numbers position by position and add up the results. It gives one number.
- sigmoid
- The function
1 / (1 + e^(-x)). It squeezes any number into the range 0 to 1.
Why it matters
A layer is many neurons reading the same inputs, each with its own weights. The next two lessons work out how a neuron's weights should change when its answer is wrong.
With a year
How it works, a worked example, the exercise with its tests, three hints and the explained solution are in the app, with a year of Algo150 for $19. The first chapter, Math foundations, is free for everyone: start with Follow a gradient.