How to Build a TinyML Project
Introduction Tiny Machine Learning (TinyML) enables machine learning models to run on microcontrollers and edge devices with limited resources. This
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Codecraft: Graphical Programming Tool for Embedded Systems
Introduction In the realm of embedded systems and IoT development, programming efficiency and ease of use are critical factors. Codecraft
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PyTorch Mobile & ONNX Runtime: AI Models for Edge Devices
Introduction The demand for running AI models on edge devices, including mobile phones, IoT devices, and embedded systems, has led
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OpenVINO Toolkit: AI Inference for Edge and Cloud Deployments
Introduction As artificial intelligence (AI) and deep learning applications expand, optimizing inference for edge and cloud environments has become essential.
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Edge Impulse: Machine Learning for Embedded & Edge Devices
Introduction Machine learning (ML) is rapidly moving beyond traditional cloud-based applications and making its way to edge devices such as
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TensorFlow Lite for Microcontrollers (TFLM)
Introduction Machine learning (ML) is transforming various industries, from healthcare to industrial automation. However, running ML models on microcontrollers has
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Cloud ML vs Edge ML: A Detailed Comparison
Introduction Machine Learning (ML) can be deployed in two primary ways: Cloud ML and Edge ML. While Cloud ML leverages
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Edge Machine Learning (Edge ML)
Introduction Edge Machine Learning (Edge ML) is revolutionizing embedded AI by enabling machine learning models to run on edge devices
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