Tiny-Classifier.cpp – Our First Tiny Classifier (kirit.com)

The article introduces "tiny-classifier.cpp," a compact C++ implementation of a simple neural network classifier inspired by Welch Labs' tutorial videos. The author revisits their early experience with neural networks from 1985 by recreating a basic model that classifies city locations based on longitude or latitude-longitude pairs. The code handles two cases: a one-dimensional input using longitudes for three cities, and a two-dimensional input using both coordinates for four cities, demonstrating flexibility without requiring code changes. This hands-on approach offers an accessible way to understand foundational neural network concepts, including input structuring, neuron design with weights and biases, layer processing, and prediction. Technically, the classifier employs single-layer networks where each neuron corresponds to a city, computing weighted sums (dot products) of inputs plus biases. The outputs are transformed using a numerically stable softmax function to yield class probabilities, enabling classification by choosing the city with the highest probability. Training data involves random sampling of geolocations for cities like Madrid, Paris, Berlin, and Barcelona, with loss assessed through cross-entropy. The article highlights practical C++ techniques such as template metaprogramming and compile-time conditionals to differentiate input dimensionality, offering a clear, low-level view of neural network mechanics. This project is significant for the AI/ML community as it bridges educational content with implementable code, empowering learners to deepen their understanding of core neural network operations without heavyweight libraries. It serves as a gentle introduction to neural modeling principles, emphasizing clarity and manual implementation of core algorithms like forward passes and softmax normalization, which are foundational to modern AI systems.