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What is Distributed Computing?

Distributed computing is a system architecture where computing resources are spread across multiple machines, working together to process tasks efficiently. It enables parallel processing, fault tolerance, and scalability, making it ideal for handling large-scale applications such as cloud services, big data analytics, and blockchain networks. Technologies like Hadoop, Kubernetes, and distributed databases power modern distributed computing environments.

Data-driven control system
A data-driven control system leverages real-time data and machine learning algorithms to optimize processes and decision-making. These systems are widely used in automation, manufacturing, and smart infrastructure applications.
Generative model
A generative model is a type of machine learning algorithm that learns from data distributions to create new, realistic samples. Examples include GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), which generate images, text, and videos for applications like AI art, deepfake technology, and content generation.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a field of artificial intelligence that enables computers to understand, interpret, and generate human language. It combines computational linguistics, deep learning, and statistical models to analyze text and speech. NLP is widely used in applications such as chatbots, machine translation, sentiment analysis, and voice assistants like Siri and Alexa.

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