Machine Learning Systems
-
Updated
Apr 12, 2026 - JavaScript
Machine Learning Systems
TinyML & Edge AI: On-device inference, model quantization, embedded ML, ultra-low-power AI for microcontrollers and IoT devices.
CS2 Skin Preview & Customization Utility for Weapons and Inventory is a visual tool for exploring and customizing weapon and inventory appearances in Counter-Strike 2, designed for previews, loadout styling, and cosmetic experimentation.
Notes and resources from Qualcomm On-device AI course, provided by DeepLearningAI
ESP32 camera that escalates from gentle reminders to airhorn if you slouch
Curated Edge AI resources for computer vision & audio: hardware, frameworks, benchmarks, literature, and communities (excluding mobile).
Estudo comparativo de arquiteturas de deep learning (CNN 1D, MLP, GRU, LSTM) para predição de temperatura em sistemas TinyML. Análise de performance, precisão e viabilidade para deploy em RP2040 com fusão de sensores AHT20/BMP280. Horizontes de 5, 10 e 15 minutos.
This is open source library for creating artificial neural network in c programming language for general purpose use.
End-to-end TinyML pipeline: gesture recognition on Arduino Nano 33 BLE Sense — 1D CNN (97.6% acc, 26.9 KB INT8) + 5 ML baselines, BLE→WebSocket→web dashboard.
Hardware-aware face detection on Samsung GT-S7392 (ARM Cortex-A9)
Fajar Lang (fj) — Systems programming language for embedded ML & OS development. Compiler-enforced safety with @kernel/@device/@safe contexts. Rust-based compiler with Cranelift/LLVM backends. Made in Indonesia.
Real-time motor speed classification using TinyML on Raspberry Pi Pico W. MLP neural network trained with TensorFlow deployed on embedded hardware (5.3 KB model). Classifies motor vibration into 4 speed levels using MPU6050 accelerometer with live OLED display feedback. Complete ML workflow from data collection to edge deployment.
Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs
Edge TinyML human activity recognition system using MPU6050 + ESP8266 with on-device SVM inference, live web output, and full data-to-deployment pipeline.
Deploy and manage ML models at the edge — OPC-UA integration, PLC connectivity, real-time inference on embedded hardware for sub-millisecond decisions
Multiposition heart sound analysis
Static ONNX graph repair tool that zero pads weight tensors to satisfy CMSIS-NN fast path alignment constraints, no retraining required.
Add a description, image, and links to the embedded-ml topic page so that developers can more easily learn about it.
To associate your repository with the embedded-ml topic, visit your repo's landing page and select "manage topics."