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Explore the challenges and solutions of deploying Neural Networks in low power edge devices with tinyML. Learn about design optimization, hardware-software co-optimization, and automatic inference code generation.
Learn to build advanced hand-gestures using radar and tinyML in less than an hour with tinyML. This study case by Imagimob demonstrates the development of gesture-controlled headphones.
Explore novel methods for compressing convolutional neural networks with tinyML's under 1-hour material. Learn to create highly accurate, compact CNN topologies for resource-limited devices.
Learn to optimize and compress neural networks for use on resource-constrained systems with tinyML's under 1-hour material. Explore pruning, quantization, and more.
Learn extreme compression techniques for LSTM models using sparse structured additive matrices in this less than 1-hour webcast by tinyML. Gain insights into structured matrices, doping, and co-matrix dropout regularization.
Explore AI and deep neural networks with tinyML's under 1-hour material. Learn about Maxim's technology for power-efficient AI at the IoT edge.
Explore AI/ML solutions for low-power Edge platforms with tinyML. Learn about challenges, opportunities, and practical applications in less than an hour.
Learn to build products using Edge AI/TinyML on MCUs with tinyML. Understand sensor optimization, component specification, and data collection cost minimization in under 1 hour.
Learn to run speech recognition models on low-power devices with Fluent.ai's Vikrant Tomar & Sam Myer in this less than 1-hour tinyML webcast.
Explore efficient multi-resolution visual computing on mobile systems with tinyML. Learn to optimize image resolution for energy saving and meet computational needs with minimal developer burden.
Explore the Akida Neural Processor's low-power, high-performance SoC for edge applications in this less than 1-hour webcast by tinyML. Learn about its unique architecture, on-chip learning, and more.
Explore software-defined imaging and adaptive video subsampling for energy-efficient object tracking with tinyML. Learn from Arizona State University's Suren Jayasuriya in under an hour.
Explore the use of Kronecker Products for compressing sequence-based neural networks in this less than 1-hour webcast by tinyML. Learn about achieving 15-38x compression factors and recovering accuracy in NLP tasks.
Learn to apply tinyML in battery-powered anomaly sensors for predictive maintenance of industrial assets with tinyML. Less than 1-hour workload.
Learn to train neural networks for sensor data in under an hour with tinyML. Understand data collection, project investigation, and achieving neural network performance for gestures.
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