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 AutoML Tutorial: Building Production-ready Models with Imagimob AI
Explore Imagimob AI platform for creating production-ready ANN models for time-series data on resource-constrained devices. Learn data visualization, preprocessing, model training, and evaluation techniques.
TinyML Talks Germany - Neural Network Framework Using Emerging Technologies for Screening Diabetic
Innovative framework using neural networks and emerging technologies for efficient screening of Diabetic Retinopathy, achieving high accuracy and significant improvements in energy consumption and latency.
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.
Battery Optimized People Counting Using FIR and AI
Explore battery-optimized people counting using FIR and AI, covering product development, power efficiency challenges, AI implementation, and quality assurance for innovative crowd monitoring solutions.
How to Build Advanced Hand-Gestures Using Radar and TinyML
Explore advanced hand gesture control using radar and Edge AI for headphones, featuring a case study and development process with Imagimob AI software.
Practical Application of TinyML in Battery Powered Anomaly Sensors for Predictive Maintenance of Industrial Assets
Explore tinyML for industrial anomaly detection, enabling predictive maintenance through low-power sensors, wireless tech, and IoT cloud integration. Includes live demo and practical insights.
Training Neural Networks for Sensors
Overview of training neural networks for gesture recognition using accelerometer data on a low-power device. Covers data collection, augmentation, network architecture, and performance for smartwatch applications.
How to Train and Deploy TinyML Models for Three Common Sensor Types
Learn to train and deploy tinyML models for sensor, audio, and vision data using Edge Impulse. Explore workflows, best practices, and optimization techniques for embedded machine learning applications.
TinyML Talks - Dr. Dominic Binks- TinyML Doesn’t Need Big Data, It Needs Great Data
Explore challenges and solutions in obtaining quality audio data for tinyML sound recognition, focusing on data sources, collection methods, labeling strategies, and performance evaluation.
KITTI-360 - A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
Comprehensive dataset and benchmarks for urban scene understanding, combining computer vision, graphics, and robotics to advance autonomous driving technology.
DALI 2018 - Andreas Geiger: How to Satisfy the Thirst for Data
Explore innovative approaches to address data scarcity in computer vision, including specialized architectures, soft constraints, self-supervision, and label transfer techniques.
Getting Started with Caffe - Deep Learning Framework Introduction - Class 3
Learn to define, train, and deploy Deep Neural Networks using Caffe framework. Covers data management, network definition, training monitoring, and deployment for classification or feature extraction.
Resource Efficient Machine Learning in a Few KBs of RAM - tinyML Summit 2020
Explore resource-efficient machine learning techniques for IoT devices with limited RAM, focusing on Microsoft's EdgeML library and algorithms like ProtoNN and FastRNN for edge computing.
The "Top" Ten tinyML Commandments
Explore ten essential principles for tinyML success, covering data engineering, codesign, metrics, reproducibility, and industry collaboration, to drive innovation in embedded machine learning.
Avoiding Loss of Quality in Tiny Models - Neuton.ai Partner Session
Explore compact AI models without sacrificing quality. Learn to assess model quality, understand decision-making logic, evaluate training data, and interpret model outputs for tinyML applications.
Lessons Learned from Building a TinyML-Powered Artificial Nose
Explore building a TinyML-powered artificial nose, from concept to implementation. Learn about hardware, sensors, models, and integrating TinyML applications into IoT ecosystems.
How Adaptive AI Solves Big Challenges for tinyML
Explore how Adaptive AI addresses challenges in tinyML, enabling efficient deployment of Edge AI models for smart devices and enterprise solutions.
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.
Cohere vs. OpenAI Embeddings - Multilingual Search
Explore multilingual semantic search using Cohere's new model, comparing it with OpenAI's GPT 3.5. Learn implementation, data preparation, vector indexing, and query techniques for enhanced search capabilities.
SQL Server Reporting Services - Data Analysis Master Tutorial
Master SQL Server Reporting Services: Learn to extend functionality, create mobile reports, and design KPIs. Gain hands-on skills in data analysis, report customization, and mobile-friendly visualizations.