The journey of a neuron | Geometric intuition

In this UA-cam video, we provide the geometric intuition behind the McCulloch Pitts neuron and the Perceptron, covering their limitations and strengths.
🧠 Limitations of the McCulloch Pitts Neuron:
The McCulloch Pitts neuron, a foundational model in artificial neural networks, has constraints. It only accepts binary inputs (0 or 1) and produces binary outputs, limiting the choices for the threshold (θ).
🔍 Exploring the Line Equation:
Consider two variables, X1 and X2. By using the McCulloch Pitts neuron, we arrive at the equation of a line. Points on or above this line are classified as 1, while those below are classified as 0. Altering the threshold (θ) demonstrates varying classification results.
🧠 Introduction to the Perceptron:
Unlike the McCulloch Pitts neuron, the Perceptron can handle real inputs, weights, and biases. While it also utilizes a line to segregate classes, it offers more flexibility with additional controls in the form of weights and biases.
🔄 Flexibility of the Perceptron:
Due to the ability to use real values for inputs, weights, and biases, the Perceptron can find multiple solutions to perform its classification task. This flexibility is a significant advancement over the McCulloch Pitts neuron.
❌ Limitation of the Perceptron:
However, similar to the McCulloch Pitts neuron, the Perceptron can only classify data when it is linearly separable. If the data is not linearly separable, the Perceptron will not be able to find a solution.
💡 This visual journey through the McCulloch Pitts neuron and the Perceptron provides a deeper understanding of their capabilities and limitations, offering insights into the evolution of neural network models.
Happy Learning!