The article delves into building a neural network from scratch using Python and Numpy, guiding readers through training and testing it on a heart disease dataset. The process involves initializing the network, performing forward and backward propagation, and adjusting parameters like learning rate and hidden layer size to enhance performance. The author compares the homemade network's accuracy with standard libraries such as scikit-learn and Keras, revealing that while the custom network performs competitively, it lacks the optimization and robustness of professional libraries. The article emphasizes the educational value of understanding neural network mechanics from the ground up, even though real-world applications typically rely on established, optimized libraries.