Topic 06: Convolutional Neural Networks - Part II

This section introduces 1D, 2D, and 3D convolutions highlighting how CNNs can adapt to sequential, spatial, and volumetric data, thus expanding their use across diverse applications. Additionally, we explore advanced CNN techniques for enhancing feature extraction, including dilated and transposed convolutions, used to expand receptive fields and upsample outputs. Furthermore, we dive into separable convolutions that improve computational efficiency by decomposing operations.