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9 articles
Learn how to set up and optimize machine learning workloads for Cerebras' CS-4 AI accelerator, leveraging its double performance capabilities through optimized data pipelines and model architectures.
Learn how to build and optimize AI inference pipelines using TensorFlow, mimicking the specialized approach of companies like Etched in the chip industry.
Learn how to build and optimize AI inference pipelines using TensorFlow, similar to what companies like Etched are developing for specialized AI chips.
Learn to implement AI-powered image enhancement techniques similar to Apple's upcoming iOS 27 Photos app, using Python, OpenCV, and TensorFlow to add realistic 'fake pixels' and enhance image quality.
Learn how to work with AI chip architectures similar to Alibaba's Zhenwu M890 by setting up your development environment, creating neural networks, and optimizing performance using Python frameworks like TensorFlow and PyTorch.
Learn how to set up a basic AI development environment and train a simple neural network using Python and TensorFlow, similar to what large data centers like Meta's Hyperion would handle.
Learn how to simulate AI chip inference execution with custom memory processing units and inference-optimized architectures, similar to Google's custom chips with Marvell.
Learn how to set up an AI development environment using cloud computing resources, similar to what Meta is investing in for its AI infrastructure.
Google has released TensorFlow 2.21, introducing LiteRT as the new universal on-device inference framework and enhancing GPU and NPU support for faster, more efficient edge deployments.
