In today's lecture, we'll dive into multilayer perceptrons (MLPs), understanding how these more sophisticated neural networks overcome the limitations of single-layer models by utilizing hidden layers, backpropagation for learning, and their ability to solve complex non-linear problems that form the foundation of modern deep learning architectures.
In today's lecture, we'll explore the foundational journey of neural networks, starting with the McCulloch-Pitts neuron model and progressing through Rosenblatt's perceptron, examining their capabilities with logic gates, understanding the XOR problem limitation, and discovering how these early challenges shaped the development of more advanced network architectures.
The review of the liner regression models in the context of machine learning and the usage of gradient descent method.
In the second part of our first lecture, we will embark on an exciting journey into the world of machine learning (ML) and the critical importance of reproducible research.