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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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applications, including search engines, chatbots, and text classification systems. It will also present and refresh the relevant deep learning architectures on which recent LLM architectures capitalise
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. This is because these companies need to allow their users to write simple, high-level code and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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architectures, diffusion models, and autoregressive techniques, as well as their applications in natural language processing, computer vision, and beyond. The course emphasizes hands-on learning, enabling
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the following areas: Software architecture and design patterns Software development lifecycle (SDLC) and agile methodologies Full-stack web development Mobile application development Teaching Experience
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architectures, diffusion models, and autoregressive techniques, as well as their applications in natural language processing, computer vision, and beyond. The course emphasizes hands-on learning, enabling
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. The ideal candidate will provide expert instruction on neural network architectures, backpropagation, activation functions, and optimization algorithms, ensuring students gain practical skills using
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. This is because these companies need to allow their users to write simple, high-level code and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework
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management Cognitive radio or adaptive communication systems, including dynamic spectrum access, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep