Show HN: Minimal DL library in C – 24 NAIVE CUDA/CPU ops, autodiff, Python API (github.com)

A new minimal deep learning (DL) library developed in C has been unveiled, presenting an engaging opportunity for those interested in both machine learning systems and GPU programming. This library features 24 basic CUDA/CPU operations, an autodiff and backpropagation engine, and a Python API for ease of use. It serves as both a practical tool for implementing neural networks and a learning resource, with comprehensive documentation, visual aids, and a forthcoming book detailing the design process and insights gained. This development is significant for the AI/ML community as it offers an accessible way to understand and experiment with core elements of deep learning architecture without the layers of abstraction often found in larger frameworks like TensorFlow or PyTorch. Users can define and train convolutional networks and multi-layer perceptrons on either GPU or CPU, visualize their computation graphs, and utilize memory management features for efficient tensor operation handling. The project emphasizes hands-on learning, as no AI assistance was employed in its creation, making it an excellent educational resource for those seeking to deepen their understanding of machine learning foundations.