Gradient Descent
Type: Concept
As of November 2026, Gradient Descent has been mentioned in 2 episodes across 2 podcasts, first heard May 2015 on ICLR. Used in the synthesis procedure to optimize images until their covariance maps match the target.
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate function.
| Mentioned in | 2 episodes on 2 podcasts (as of November 2026) |
|---|---|
| First heard | May 2015 on ICLR |
| Field | Mathematics |
| Domain | Optimization |
What do podcasts say about Gradient Descent?
2 quotes from Unknown (Real Engineering narrator/host), recorded on ICLR and Real Engineering.
Do gradient descent on this image.
ICLR · · Hear itPresented as foundational: the backbone of neural networks… the most used learning algorithm.
Unknown (Real Engineering narrator/host) · Real Engineering · · Hear it
How do different shows talk about Gradient Descent?
2 shows discussed Gradient Descent in these episodes.
- Real Engineering — 1 episode
Presented as foundational: the backbone of neural networks… the most used learning algorithm.
How Machine Learning is Fighting Cancer - ICLR — 1 episode
Do gradient descent on this image.
ICLR2015-olivier-henaff
Who talks about Gradient Descent on podcasts?
Unknown (Real Engineering narrator/host) brought up Gradient Descent; the most recent mention was Oct 2018 on Real Engineering.
- ICLR2015-olivier-henaff on ICLR (May 2015)
- How Machine Learning is Fighting Cancer on Real Engineering (Oct 2018) — Unknown (Real Engineering narrator/host)
What is Gradient Descent mentioned alongside?
Gradient Descent is most often mentioned alongside Unsupervised learning problem, Gaussian scale mixture and Wavelet decomposition.
Mentioned together: Unsupervised learning problem, Gaussian scale mixture, Wavelet decomposition, Gaussian white noise, Eigenvalue decomposition, Hierarchical representation, Nuclear norm, Local covariance matrices, Heavy tails, Statistical observations