Understanding Different Learning Methods in AI

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Artificial Intelligence (AI) has transformed the way we interact with technology, enabling automation, decision-making, and predictive analytics across various industries. At the core of AI development are different learning methodologies that dictate how models learn from data. In this blog, we will explore the key learning methods used in AI, their typical applications, how they […]

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Benchmarking AI Workloads on Intel Xeon with AMX: Best Practices and Performance Insights

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Introduction Artificial Intelligence (AI) workloads increasingly depend on robust computational resources, and Intel Xeon processors present an attractive solution for both training and inference. The introduction of Advanced Matrix Extensions (AMX) in Intel Xeon has significantly enhanced AI acceleration, especially for deep learning, natural language processing, and high-performance computing applications. Accurate benchmarking of these workloads […]

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DeepSeek and the AI Hardware Market: A Disruptor or Just Another Competitor?

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The AI hardware market is rapidly evolving, driven by the increasing complexity of AI workloads. DeepSeek, a new large-scale AI model from China, has entered the scene, but its impact on the broader AI landscape remains an open question. Is it simply a competitor to OpenAI’s ChatGPT, or does it have wider implications for inferencing, […]

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The Decline of TensorFlow and the Rise of PyTorch in AI Workloads: A Documented Shift

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TensorFlow, formerly the preeminent deep learning framework created by Google, has been eclipsed in recent years by PyTorch, developed by Facebook AI Research. Previously the unequivocal choice for researchers and corporations, TensorFlow has experienced a slow decrease as PyTorch has emerged as the favoured framework for AI workloads. This transition is seen not only in […]

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