  Throughout the last years, machine learning techniques have been broadly
encouraged in the context of deep learning architectures. An exciting algorithm
denoted as Restricted Boltzmann Machine relies on energy- and
probabilistic-based nature to tackle the most diverse applications, such as
classification, reconstruction, and generation of images and signals.
Nevertheless, one can see they are not adequately renowned compared to other
well-known deep learning techniques, e.g., Convolutional Neural Networks. Such
behavior promotes the lack of researches and implementations around the
literature, coping with the challenge of sufficiently comprehending these
energy-based systems. Therefore, in this paper, we propose a Python-inspired
framework in the context of energy-based architectures, denoted as Learnergy.
Essentially, Learnergy is built upon PyTorch to provide a more friendly
environment and a faster prototyping workspace and possibly the usage of CUDA
computations, speeding up their computational time.
