What Is an Activation Function and Why It Is Needed
About this video
Why a non-linear activation function makes deep learning possible.
An activation function is the nonlinear operation each neuron applies to its weighted sum z to produce its output a equals f of z. Without it, stacking layers only chains linear transformations and the whole network collapses into a single one. That nonlinearity is what lets a network learn complex patterns.
Video transcript
- 0:01 Sigmoid, tanh and ReLU bend the straight line.
- 0:04 Without that bend, stacking layers adds up to a single linear layer; with it, the
- 0:09 network learns complex patterns.
More videos
Explainer
Firecrawl: Web Data for Agents
If you write your own crawler: robots.txt, one queue per host and 304 Not Modified make the polite option the cheap one too.
Explainer
How to Install SearXNG with Docker
One use for your own instance: an AI agent that finds its own sources by querying SearXNG, without paying per search.
Explainer
The Computational Graph and Automatic Differentiation
The computational graph and automatic differentiation, in action.
Explainer
Backpropagation: The Step-by-Step Derivation
The backpropagation derivation, factor by factor with the chain rule.