  Quantum computing's promise lies in its intrinsic complexity, with
entanglement initially heralded as its hallmark. However, the quest for quantum
advantage extends beyond entanglement, encompassing the realm of nonstabilizer
(magic) states. Despite their significance, quantifying and characterizing
these states pose formidable challenges. Here, we introduce a novel approach
leveraging Convolutional Neural Networks (CNNs) to classify quantum states
based on their magic content. Without relying on a complete knowledge of the
state, we utilize partial information acquired from measurement snapshots to
train the CNN in distinguishing between stabilizer and nonstabilizer states.
Importantly, our methodology circumvents the limitations of full state
tomography, offering a practical solution for real-world quantum experiments.
In addition, we unveil a theoretical connection between Stabilizer R\'enyi
Entropies (SREs) and the expectation value of Pauli matrices for pure quantum
states. Our findings pave the way for experimental applications, providing a
robust and accessible tool for deciphering the intricate landscape of quantum
resources.
