What role do libraries like TensorFlow and PyTorch serve in AI development?

Libraries such as TensorFlow and PyTorch play a crucial role in AI development by providing frameworks specifically designed for building and designing machine learning and deep learning models. These frameworks offer pre-built functions, tools, and structures that facilitate various tasks involved in the AI development process, such as data manipulation, model training, and deployment.

With TensorFlow and PyTorch, developers can implement complex algorithms with greater ease and efficiency. They also include resources for creating neural networks, performing mathematical computations, and handling data input/output. By streamlining these processes, these libraries allow developers to focus more on the innovation and testing of AI models rather than getting bogged down in the low-level details of programming AI from scratch.

In contrast, coding languages for app development do not inherently include the specialized tools and functions required for AI model construction and training. Similarly, server management pertains to the deployment and operation of applications rather than the development of AI models, while graphic design for AI products is an entirely different discipline focused on user interfaces and visual elements rather than the core algorithms and data processing needed for AI.