What is it?
What approaches as : the long-run behaviour of a function, and of an algorithm's cost as inputs grow.
Formulas
Where it shows up in computing
when stays between positive constants as .
Where it shows up in AI
The sigmoid saturates: as its slope tends to 0, the root of vanishing gradients.
Where is it used?
Computing topics reachable from here, through the chain of ideas that leads to them:
ℒ AI and machine learning
- Improper integrals→Probability density function→Bayesian inference★★★★★
- Improper integrals→Probability density function→Generative models★★★★★
- Improper integrals→Probability density function→Expectation→Loss function★★★★★
- Improper integrals→Probability density function→Maximum likelihood estimation→Logistic regression★★★★★
- Improper integrals→Probability density function→Expectation→Stochastic gradient descent (SGD)★★★★★
- Improper integrals→Probability density function→Expectation→Reinforcement learning★★★★★
- +7
∿ Signals, media and vision
- Improper integrals→Fourier transform→Signal processing★★★★★
- Improper integrals→Fourier transform→Sampling theorem (Nyquist–Shannon)★★★★★
- Improper integrals→Fourier transform→Fast Fourier transform (FFT)★★★★★
- Improper integrals→Fourier transform→Telecommunications (modulation, OFDM)★★★★★
- Improper integrals→Laplace transform→Z-transform→Digital filters★★★★★
- Improper integrals→Fourier transform→Signal processing→Media compression (JPEG, MP3, video)★★★★★
- +1
What depends on it
This page has the essentials. A fuller treatment (intuition, formal definition, worked example) is on the way.