TinyML emotion detection lets a small micro‑controller read facial emotions in real time, all on a coin‑cell battery. The guide covers hardware, dataset creation, model training, quantization, and deployment on ESP32‑C3.
AI‑powered security monitoring uses machine learning to spot hidden threats in network logs, reducing false alarms and catching new attacks before they cause damage.
Federated learning for edge devices lets tiny gadgets learn together while keeping data private. This guide covers the basics, tools, and a smart‑home example.
TinyML on-device models let tiny devices run AI locally for privacy, low latency, and low cost. This guide covers tools, design tips, and a sample wake word project.
TinyML lets you run neural networks on tiny microcontrollers, enabling smart sensors that keep data local and save power. This guide walks through a voice‑activity demo on ESP32‑S3 and covers best practices.
A practical guide to building, optimizing, and deploying Edge AI for IoT on low‑power devices. Learn how to train, convert, and run models on Raspberry Pi, ESP32, and more.
AlphaGenome uses a hybrid convolutional and transformer architecture plus RAG integration to boost genomics research, from precision medicine to gene therapy.