Python-Based Deep Learning: Advances, Challenges, and Sustainable Approaches
DOI:
https://doi.org/10.15680/IJCTECE.2024.0701002Keywords:
Deep Learning, Python, Sustainability, Model Optimization, Energy Efficiency, Neural Networks, AI Algorithms, Python Libraries, Green Computing, Computational ChallengesAbstract
Deep learning has emerged as a transformative technology, enabling advancements in fields such as computer vision, natural language processing, and autonomous systems. Python, with its comprehensive libraries and frameworks, has become the primary language for developing deep learning models. This paper explores the latest advancements in Python-based deep learning, focusing on key frameworks, algorithms, and innovations. It also discusses the challenges associated with implementing deep learning solutions, such as computational cost, data quality, and model interpretability. Furthermore, it addresses sustainable approaches to deep learning, emphasizing energy-efficient techniques, model optimization, and the adoption of green computing practices. By understanding these advancements and challenges, we can push forward towards more efficient and sustainable deep learning solutions using Python.
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