Abstract

Deep learning is becoming more and more popular in a wide variety of machine learning research. Since its success, deep learning has mainly been used for cognitive tasks such as speech and visual recognition, while lately its research is being applied to a wider range of applications including natural language processing and reinforcement learning. In parallel to the progress of deep learning methods, many programming frameworks are being developed to satisfy the demands of researchers and practitioners in the field. These frameworks are generally compared in terms of performance and the programing paradigms they are based on, which also have seen rapid advancement in the last few years. Thus, proper understanding of the different features and capabilities of these frameworks, becomes very important when selecting the appropriate framework to use for implementing desired neural networks.