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.

中文翻译：

---

Learnergy：基于能量的机器学习器

在过去的几年里，机器学习技术在深度学习架构的背景下得到了广泛的鼓励。一种令人兴奋的算法称为受限玻尔兹曼机，它依赖于基于能量和概率的性质来处理最多样化的应用，例如分类、重建以及图像和信号的生成。然而，与其他众所周知的深度学习技术（例如卷积神经网络）相比，人们可以看到它们的知名度并不高。这种行为导致缺乏围绕文献的研究和实施，以应对充分理解这些基于能量的系统的挑战。因此，在本文中，我们在基于能源的架构的背景下提出了一个受 Python 启发的框架，表示为 Learnergy。本质上，