Neural Networks
Explore how neural networks function starting with perceptrons, the foundational nodes inspired by brain neurons. Understand weight learning, activation functions like sigmoid, and the limitations of single-layer perceptrons, including their inability to solve non-linear problems such as XOR. This lesson prepares you to dive deeper into multi-layer networks for complex problem solving.
We'll cover the following...
Perceptrons
We already looked at the general architecture of neural networks in the last lesson. Let’s discuss how a neuron works and how a node in a neural network is inspired by the working of neurons in the brain. Rosenblatt proposed the model of a simple perceptron in 1957, which serves as the building blocks of today’s artificial neural networks (ANNs).
In the above diagram, we have an input layer which consists of features. Weights ...