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GRU ARCHITECTURE RESEARCH IN THE CONTEXT OF PYTHON: APPROACHES AND APPLICATIONS

Istamqulov Hasanjon   (Ph.D. candidate Khujand State University named after ac. B. Gafurov, Tajikistan )

This article explores the analysis of Gated Recurrent Unit (GRU) networks using Python, focusing on their application in deep learning for processing sequential data. It begins with an overview of recurrent neural networks (RNNs) and the rationale behind GRUs. The article then delves into the architecture of GRUs, illustrating their unique features such as reset and update gates. It provides a practical guide to implementing GRU networks in Python with popular libraries like TensorFlow and Keras, including sample code for building and evaluating models. The article also compares GRUs with other RNN variants, highlighting their advantages in various scenarios. In conclusion, it underscores the significance of GRUs in handling temporal dependencies and their potential in future deep learning research.

Keywords:Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Python, Deep learning, Temporal dependencies, TensorFlow, Keras, Model evaluation, Neural network architecture
 

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Citation link:
Istamqulov H. GRU ARCHITECTURE RESEARCH IN THE CONTEXT OF PYTHON: APPROACHES AND APPLICATIONS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2024. -№05. -С. 83-86 DOI 10.37882/2223-2966.2024.05.14
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