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Version control refers to the systematic tracking and management of changes to code, documents, and other digital assets within a project. It enables developers and researchers to collaborate effectively, maintain the integrity and reproducibility of their work, and easily revert to previous versions if necessary. In the context of deep learning, version control plays an essential role in managing the complex and ever-evolving nature of machine learning models, datasets, and experiments. By using version control systems like Git or Mercurial, practitioners can maintain a clear record of modifications, compare different iterations, and efficiently coordinate their efforts. This helps ensure that deep learning projects remain organized, up-to-date, and accessible to both individual contributors and larger teams, ultimately facilitating the development and deployment of more robust and reliable AI systems within the broader technology and web development landscapes.