Data Augmentation: Deep Learning

Related lectures (32)

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Explores Galilean transformations in spacetime, focusing on measurements and coordinate transformations.

Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores noise in electronics, covering average power, signal-to-noise ratio, deterministic and random signals, and noise amplification.

Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.

Explores the impact of noise in microelectronics on various circuit stages and devices.

Covers the concept of affine transformations in functions, including shifting and compressing functions.

Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.