Week #5 - Introduction to Neural Networks

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.

Learning objectives: - Explain the historical significance of the McCulloch-Pitts neuron model (1943) and describe its basic components, including binary inputs, threshold activation, and binary output. - Compare and contrast the McCulloch-Pitts neuron with Rosenblatt's perceptron model, highlighting key advancements such as weighted connections and the learning algorithm. - Demonstrate how to solve linearly separable problems using the perceptron model through simple examples like AND and OR gates. - Analyze why the XOR problem cannot be solved by a single perceptron, using geometric visualization to explain the concept of linear separability. - Evaluate the limitations of early neural network models and explain how these limitations influenced the development of multilayer networks.

Laboratory

https://classroom.github.com/a/2BzNhU74