Data Augmentation: Deep Learning

Related lectures (32)

Page 3 of 4

Covers 2D transformations, linear maps, matrices, affine transformations, and orthogonal transformations.

Explores Gaussian processes, linear systems, transformations, and noise properties in multivariable control applications.

Discusses various noise sources in optical detectors, including shot noise, thermal noise, and their implications on detection performance.

Explores affine coordinate transformation, satellite tracking, and challenges in map projections.

Explores the georeferencing of images, covering methodology steps, transformations, resampling, and practical application using QGIS software.

Explores noise sources in data acquisition, including thermal, shot, and amplifier noise, as well as the effects of ADC noise.

Explores weight sharing, filters, channels, and data augmentation in convolutional neural networks.

Explores the similarity of convex bodies, affine transformations, the Johen's Theover theorem, and KKT conditions.

Explores image processing techniques such as contrast manipulation, enhancement, and sharpening using Fiji software.

Covers noise analysis in circuits, including small-signal and input-referred noise, with examples of amplifiers and filters.