Neural Networks: Basics and History

This lecture introduces the basics of neural networks, focusing on their history and key concepts. Starting with the historical development of neural networks, the instructor covers the fundamental principles, including the mathematical model of a neuron and the artificial neuron. The lecture then delves into gradient descent, explaining how it is used to update the parameters of neural networks. Special attention is given to the multilayer perceptron (MLP) and its training phases. The presentation concludes with a discussion on the importance of cross-validation and the upcoming topic of building convolutional neural networks.