Part 4/9:
The architecture of deep neural networks has deep roots in neuroscience. Pioneering work by Hubel and Wiesel in the 1960s laid the groundwork for understanding how the brain processes visual information. Their discovery of distinct types of cells in the visual cortex inspired the design of artificial neural networks that alternate between layers focusing on feature selectivity and layers that provide invariance to nuisance variations.
Neural Networks Explained
A neural network processes input data through multiple layers, employing both linear and non-linear transformations. This layered approach enhances the network's ability to capture complex features and patterns, making it significantly more powerful than traditional algorithms.