Simple Introduction to the Arduino
Learn to write your first Arduino program, blinking an LED on the circuit board. Step-by-step guide covers Arduino environment, pin modes, variables, and constants.
Speech-to-Intent on MCU: TinyML for Efficient Device Control - Lecture 6
Learn efficient speech-to-intent techniques for MCUs, focusing on direct parsing of user utterances into actionable output. Explore model training, deployment, and testing on Wio Terminal.
Wio Terminal TinyML Weather Prediction with TensorFlow Lite and LVGL GUI - Course 4
Create an intelligent meteostation using Wio Terminal and TensorFlow Lite for Microcontrollers to predict weather and precipitation based on local environmental sensor data, with LVGL GUI integration.
Machine Learning Inference on Raspberry Pi Pico 2040 via Edge Impulse
Learn to implement machine learning inference on Raspberry Pico 2040 using Edge Impulse, covering environment setup, sample data inference, sensor data processing, and porting details.
Wio Terminal TinyML Course - Audio Scene Recognition on Microcontrollers with Arduino IDE
Learn to implement audio scene recognition on microcontrollers using TinyML, Arduino IDE, and Wio Terminal. Covers data collection, processing, model training, deployment, and mobile notifications with Blynk.
Introduction to TinyML on Wio Terminal - Machine Learning on Microcontrollers
Introduction to TinyML on Wio Terminal: Learn to implement machine learning on microcontrollers using Arduino IDE, covering data gathering, processing, model training, and deployment.
TinyML Auto ML Deep Dive: Streaming Analytics for Edge Devices
Explore streaming analytics for edge devices with Stream Analyze's platform. Learn to develop, deploy, and monitor edge AI solutions using real-time data processing and on-device intelligence.
On-Device Learning Under 256KB Memory - Challenges and Solutions for IoT Devices
Explore on-device learning for IoT devices with limited memory. Learn techniques for efficient model adaptation, quantization-aware scaling, and sparse updates to enable continuous learning on tiny hardware.
Sensing Applications as a Driver for TinyML Solutions
Explore intelligent MEMS applications in motion learning, sports analytics, and environmental sensing. Learn how domain knowledge optimizes TinyML for ultra-low power contexts and shapes emerging ecosystems.
1 kB and Not a Bit More - The Ideal Weight for a TinyML Model
Explore automated creation of ultra-compact ML models for tiny smart devices, enabling efficient embedding in memory-constrained hardware without data science expertise.
Deploy AI with AWS ML and IoT Services on Jetson Nano
Learn to deploy AI models on Jetson Nano using AWS ML and IoT services. Explore SageMaker, Greengrass, and real-time video processing for edge computing applications.
Improving Energy Efficiency and Robustness of tinyML Computer Vision Using Log-Gradient Input Images
Explore innovative techniques to enhance energy efficiency and robustness in tinyML computer vision, focusing on log-gradient input images and architecture optimization for microcontrollers.
Accelerate ML Development With Cloud-Based Arm Cortex-M Models
Explore cloud-based Arm Cortex-M models for accelerated ML development, featuring Virtual Hardware integration, Python-scriptable peripherals, and TensorFlow optimizations.
Building and Enabling Voice Control with ARM Cortex-M
Explore Sensory's Edge-AI technology for creating custom wake words and voice control on ARM Cortex-M, with a focus on VoiceHub platform for developers.
AIfES - An Open-Source Standalone AI Framework for Almost Any Hardware
Explore AIfES, an open-source AI framework for embedded systems, enabling neural network deployment and training on various hardware, including 8-bit microcontrollers and Arduino boards.
Binarized Neural Networks on Microcontrollers
Explore Binarized Neural Networks for efficient deep learning on microcontrollers, enabling complex tasks with minimal memory and high performance using 1-bit encoding for weights and activations.
Tiny but Powerful: Hardware for High Performance, Low Power Machine Learning
Explore cutting-edge hardware for ultra-low power machine learning at the edge, covering TinyML applications, model optimization, and emerging trends in efficient computing.
Meet the Engineers: Exploring the Raspberry Pi Pico and RP2040 - Part 1
Explore the Raspberry Pi Pico and RP2040 with industry leaders in this engaging webinar, covering technical aspects, design choices, and potential applications of these innovative microcontrollers.
Build Industrial-Grade TinyML Applications with Edge Impulse - Tutorial
Learn to build industrial-grade tinyML applications using Edge Impulse. Covers dataset collection, model design, training, optimization, and integration into embedded systems running on MCUs.
Building a Magic Wand with TensorFlow Lite Micro - Tutorial
Learn to build an IMU-based gesture recognition model for Arduino, covering data gathering, training, and deployment using TensorFlow Lite Micro and CMSIS-NN library.
Reprogramming CPU Microcode With an Arduino
Explore microcode reprogramming using Arduino, covering EEPROM programming, constants definition, and NoOp instructions for enhanced CPU control and customization.