A Framework for Distributed Deep Learning Layer Design in Python
This work addresses the need for efficient DNN layer design tools for researchers and practitioners, but it appears incremental as it builds on existing Python-based ML frameworks.
The paper tackles the problem of designing deep neural network layers in Python by presenting a framework for testing DNN designs, and it demonstrates the system's effectiveness through experimental results.
In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and some experimental results are presented to prove the effectiveness of the system.