Zenkai is an open-source framework designed to give researchers more control and flexibility over building and training deep learning machines. It does this by dividing the deep learning machine into layers of semi-autonomous learning machines with their own target and learning algorithm. This is to allow researchers greater exploration such as the use of non-differentiable layers or learning algorithms beyond those based on error backpropagation. Backpropagation Rumelhart et al. [1986] has powered deep learning to become one of the most exciting fields of the 21st century. As a result, a large number of software tools have been developed to support efficient implementation and training of neural networks through the use of backpropa- gation. While these have been critical to the success of deep learning, building frameworks around backpropagation can make it challenging to implement solutions that do not adhere to it. Zenkai aims to make it easier to get around these limitations and help researchers more easily explore new frontiers in deep learning that do not strictly adhere to the backpropagation framework.

翻译：Zenkai是一个开源框架，旨在为研究人员提供对深度机器学习构建与训练过程的更强控制与灵活性。其核心思想是将深度机器学习系统分解为多个半自主的学习层，每层拥有独立的目标函数和学习算法，从而支持研究人员开展更广泛的探索，例如使用不可微分层或基于误差反向传播之外的学习算法。反向传播（Rumelhart等人，1986）推动深度学习成为21世纪最激动人心的领域之一。为此，大量软件工具被开发出来，通过反向传播实现神经网络的高效构建与训练。尽管这些工具对深度学习的成功至关重要，但基于反向传播构建的框架可能会使实现不遵循该范式的解决方案变得困难。Zenkai旨在降低突破这些局限的难度，帮助研究人员更便捷地探索不严格遵循反向传播框架的深度学习新前沿。