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TensorFlow is widely recognized as a powerful library specifically designed for deep learning applications within the field of artificial intelligence. Its architecture allows for the building and training of complex neural networks, which are fundamental to deep learning. TensorFlow supports both CPUs and GPUs, enabling scalable and efficient computation for large datasets and intricate models.

The library offers a variety of tools for model development, including high-level APIs like Keras that simplify the process of designing neural networks, thus making it accessible for users with varying levels of expertise. TensorFlow's extensive documentation and active community contribute to its popularity, making it a go-to choice for researchers and practitioners looking to implement deep learning methods effectively.

Other libraries, while important in their own right, serve different primary purposes. For example, SciPy is primarily focused on scientific and technical computing, NumPy provides support for large, multi-dimensional arrays and matrices, and Pandas specializes in data manipulation and analysis. While these libraries can complement deep learning tasks, they do not have the specialized framework and features specifically tailored for building and training deep learning models like TensorFlow does.