Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Introduces fundamental notions in digital filtering, covering 2D filtering approaches, linear filters, stability, FIR and IIR filters, frequency domain filtering, and Gaussian filters.