Looking back at my senior capstone project at Missouri State — building a real-time American Sign Language smart home controller with MediaPipe, custom CNNs, and networking — and how it sparked my path in machine learning.
An empirical investigation comparing PyTorch 2.14, JAX 0.11, and TensorFlow across five model architectures and four batch sizes, measuring compiler speedups, kernel overheads, and hardware limits on Apple Silicon.
A framework-neutral Python library that estimates a neural network's FLOPs and tensor traffic, measures its runtime, and uses a roofline model to tell you exactly where performance is being left on the table.