TensorFlow vs Theano: Issues and Challenges for Natural Language Processing

ensorFlow and Theano were popular deep learning frameworks for building and training neural networks, including image classification and NLP models. TensorFlow's evolution from a static graph-based approach to incorporating Eager Execution is examined, shedding light on its impact on ease of use and dynamic operations. Deep learning frameworks play a pivotal role in shaping the landscape of machine learning research and applications. Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, owing much of its progress to deep learning frameworks. This paper presents an in-depth comparative analysis of two influential deep learning frameworks, TensorFlow and Theano, with a specific focus on their issues and challenges in the domain of NLP. Through a comprehensive exploration, this study aims to provide NLP practitioners and researchers with insights to navigate the complexities of framework selection for their linguistic tasks. By distilling the nuances of TensorFlow and Theano in the context of NLP challenges, this research equips NLP practitioners with a well-rounded understanding of the trade-offs and considerations in framework selection. Findings contribute to the informed decision-making process for choosing the appropriate framework based on the specific requirements of NLP tasks.