The library implements n-dimensional array operations modeled after NumPy and PyTorch, enabling automatic differentiation for gradient-based optimization. It supports multiple backends including CUDA and OpenCL for GPU acceleration, and OpenMP for CPU parallelization. Arraymancer includes built-in machine learning algorithms such as PCA, classifiers, regressors, and clustering, alongside deep learning components for neural network construction.