Part 3/12:
Moving through history, Gurban discusses pivotal developments, such as the introduction of Markov models in the early 1900s that could generate sequences, and the perceptron in the 1940s, which imitated retinal functions. The evolution continued with advancements in genetic algorithms, self-organizing systems, and core neural network architectures including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers.