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.
Oral Tongue Lesion Detection Using TinyML on Embedded Devices
Explore oral tongue lesion detection using TinyML on embedded devices for early cancer screening, supporting physicians in remote healthcare settings with limited specialized resources.
TinyML Talks Pakistan - SuperSlash - Unifying Design Space Exploration and Model Compression
Unifying design space exploration and model compression for efficient deep learning accelerators on resource-constrained devices, optimizing off-chip memory access and model size.
Accelerate Edge Compute with Syntiant's TinyML Platform
Explore Syntiant's TinyML platform for efficient edge AI in battery-powered devices, featuring Neural Decision Processors for enhanced processing and power efficiency compared to MCUs.
Endpoint AI Revolution Driven by Standardized Computing Platform
Explore the Endpoint AI revolution with Arm's standardized computing platform, focusing on TinyML applications, robust software stacks, and specialized processors for efficient machine learning on IoT devices.
Chasing Cows - Making Africa Smarter With Embedded ML
Explore embedded ML applications in African agriculture and conservation, from cow health monitoring to elephant tracking, addressing unique challenges and local solutions.
TinyML for Good: Ready to Take Off for a Big Impact
Explore tinyML's potential for positive impact through expert insights on vision, value, device recommendations, education, and involvement opportunities.
AutoML + TinyML with Edge Impulse's EON Tuner
Explore AutoML for TinyML with Edge Impulse's EON Tuner. Learn how to optimize embedded machine learning models within device constraints for innovative applications.
TinyML Talks Morocco - Enabling Ultra-Low Power Always-On Computer Vision at Qualcomm
Exploring ultra-low power computer vision for TinyML applications, focusing on Qualcomm's innovations in sensors, hardware, and algorithms to enable always-on vision at 1mW power consumption.
TinyML Talks Kenya - TinyML and the Developing World
Explore TinyML's potential in developing countries for medicine, agriculture, and conservation. Learn about low-cost, battery-powered machine learning applications adaptable to local needs.
Edge Machine Learning for Mobile Health Technologies
Edge machine learning techniques for resource-constrained mobile health and wearable technologies, addressing challenges and opportunities in IoT systems for real-time health monitoring and early detection of abnormalities.
Management of IoT TinyML Devices
Explore integrating LwM2M protocol with TinyML for efficient IoT device management, enabling streamlined OTA updates, telemetry, and anomaly detection on resource-constrained devices.
Honey, I Shrunk the TinyML
Explore TinyML: train, convert, and run machine learning models on microcontrollers. Learn quantization, pruning techniques, and step-by-step integration for embedded AI applications.
TinyML and Efficient Deep Learning Computing - Course Summary
Explore efficient machine learning techniques for deploying neural networks on resource-constrained devices, covering model compression, pruning, quantization, and more.
TinyML and Efficient Deep Learning Computing - Lecture 24: Course Summary
Explore efficient machine learning techniques for deploying neural networks on resource-constrained devices. Gain hands-on experience with mobile AI and quantum machine learning.
It Worked When I Prompted It - Challenges in LLM Production Deployment
Explore challenges and solutions for deploying LLMs in production, covering design patterns, vector databases, reliability enhancement, and cost management strategies.
Next-Level Parameter Sharing - The Future of AI Collaboration
Explore cutting-edge multi-task learning techniques for tinyML, focusing on hard contextual parameter sharing to enhance AI collaboration and efficiency in resource-constrained environments.
Deploying TinyML Models at Scale: Insights on Monitoring and Automation
Insights on scaling TinyML deployments, emphasizing continuous model monitoring, data preparation, and automated ML development for reliable IoT applications in diverse environments.
Distributed Training and Gradient Compression - Lecture 14
Explore distributed training challenges, focusing on bandwidth and latency bottlenecks. Learn gradient compression techniques and delayed gradient averaging to optimize communication in deep learning.
AI Model Efficiency Toolkit (AIMET) - Lecture 25
Explore AI model efficiency techniques with Qualcomm's toolkit. Learn about adaptive rounding, autocon, and transformer quantization for optimized inference and training on resource-constrained devices.