This tutorial demonstrates how to build a neural network that classifies iris species using petal and sepal widths as inputs. The author provides Python code with specific weights and biases, showing how a simple feedforward network processes two features through hidden nodes with ReLU activation.

The code calculates outputs for Setosa, Versicolor, and Virginica species, revealing how different weight configurations produce distinct prediction surfaces. For the example input (0.5 petal, 0.37 sepal), the model outputs 0.86 for Versicolor, correctly identifying the flower type through this computational approach.

This practical walkthrough bridges theory and implementation, showing developers how to manually construct forward passes. The author notes future discussions will cover Argmax and Softmax for multi-class decisions, providing a foundation for more complex neural architectures in machine learning projects.