Layers and activation functions

After this lesson you can: Say why a network without activations collapses to a single layer.

First: What a neuron actually computes

Watch

• But what is a neural network? | Deep learning chapter 1 - 3Blue1Brown, 19 min. Strong visual conceptual background on networks, useful context though less specifically about activation functions.
• Activation Functions In Neural Networks Explained | Deep Learning Tutorial - AssemblyAI, 7 min. Focused activation-function explainer from a practical AI channel, well matched to why layers need nonlinearity.
• Activation Functions | Deep Learning Tutorial 8 (Tensorflow Tutorial, Keras & Python) - codebasics, 16 min. Beginner-friendly deep-learning tutorial likely connects activations to implementation without being too broad or mathematical.

Notes

A layer takes the numbers from the previous layer, mixes them with weights, adds biases, and produces new numbers. If you stop there, the layer is only doing a linear transformation: stretching, rotating, shifting, and recombining the input space. An activation function is the extra step that bends this result before passing it onward, usually by applying a simple nonlinear rule to each neuron’s output.

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