Matrix Capsule Networks with EM Routing, introduced by Hinton et al. (2018a), offer a powerful way to model part-whole relationships and pose information in neural networks. However, their broader adoption has been limited, possibly due in part to the complexity of the model and inconsistencies between the original paper and its official implementation. In this thesis, we present a faithful and modular reimplementation of the Matrix Capsule Network in TensorFlow 2, addressing missing and unclear aspects of the original description. Our implementation achieves competitive results on the SmallNORB dataset and is designed to be accessible, adaptable, and well-documented. By clarifying key architectural details and providing open-source code, we aim to support further research and lower the barrier to working with capsule-based models.