Fourier Features: Solving Spectral Bias in Neural Networks

Teaching a neural network to render the Mandelbrot set reveals a fundamental flaw in standard MLPs: spectral bias. By implementing Multi-Scale Gaussian Fourier Features, we can transform blurry approximations into sharp fractal boundaries. Learn why input representation matters more than model depth when dealing with high-frequency data and complex coordinate-based systems.

Retrieval-Augmented Forecasting: Improving Time-Series Model Accuracy

Retrieval-Augmented Forecasting (RAF) is revolutionizing time-series analysis by adding an explicit memory step to traditional models. Instead of relying on static training weights, RAF allows models to perform similarity searches on historical data, significantly improving accuracy during rare events and market shifts. Learn how to implement vector-based memory for more robust forecasting.

Why RAG Chunk Size is the Most Critical Variable You’re Ignoring

Understanding RAG chunk size is critical for AI retrieval stability. This article breaks down how different chunking strategies—from small 80-character fragments to large 500-character blocks—impact the accuracy of your vector search. Learn how to refactor your text-splitting logic in PHP to avoid context loss and reduce AI hallucinations in your WordPress integrations.