CNNs are characterized by their use of a group of neurons typically referred to as a filter or kernel. In image recognition, this kernel is repeated over the entire image. In this way, CNNs may achieve the property of translational invariance - once trained to recognize certain things, changing the position of that thing in an image should not disrupt the CNN's ability to recognize it. In this episode, we discuss a few high-level details of this important architecture.
Fler avsnitt av Data Skeptic
Visa alla avsnitt av Data SkepticData Skeptic med Kyle Polich finns tillgänglig på flera plattformar. Informationen på denna sida kommer från offentliga podd-flöden.
