Building the Simplest Neural Network Possible

In this video, we dive into the simplest form of a neural network: the perceptron.

We'll explore how a single neuron, using a basic step activation function, can solve a fundamental logic problem. Using just one neuron with two inputs, we demonstrate how to compute an AND gate, showing how neural networks process binary outcomes with weighted inputs and a bias term.

You’ll learn:

• What a Perceptron is: The most basic neural network architecture.

• How AND Gates Work in Neural Networks: Applying a perceptron to achieve binary logic operations.

• Core Neural Network Concepts: Weighted sums, biases, activation functions, and feedforward.

This walkthrough is perfect for beginners in machine learning and deep learning, setting the foundation for understanding more complex networks. Join us as we build this simple model and discuss what comes next—training, cost functions, and more advanced neural architectures.

Follow my learning journey on YouTube & GitHub

For all video content head to my YouTube Channel.

As a data engineer on an AI research team, I am learning how to build deep neural networks and sharing the journey on the way.

For my code and notes on what I am learning head to https://github.com/bitsofchris/deep-learning to follow along.