Show HN: Autograd.c – a tiny ML framework built from scratch (github.com)

A new machine learning framework called Autograd.c has been introduced, showcasing a minimalist approach to building ML tools from the ground up. The framework emphasizes efficiency, operating "close to metal," and it facilitates the entire ML pipeline from data loading to model training, as demonstrated with a CIFAR-10 dataset example. Initial results indicate an accuracy of approximately 23.56% after training, with a recorded loss improving over epochs, underscoring its foundational capabilities. The significance of Autograd.c lies in its lightweight architecture, allowing developers to engage with ML concepts at a granular level. This DIY ethos not only aids in deepening understanding of machine learning principles but also offers a customizable alternative to heavier frameworks like TensorFlow or PyTorch. The project's transparency in accepting community feedback enhances collaborative development, positioning Autograd.c as a valuable tool for both newcomers and seasoned practitioners in the AI/ML field.