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Rapid Prototyping on NVIDIA Jetson Platforms with MATLAB

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This article discusses how an application developer can prototype and deploy deep learning algorithms on hardware like the NVIDIA Jetson Nano Developer Kit with MATLAB. In previous posts, we explored how you can… Rapid Prototyping on NVIDIA Jetson …

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“Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB,” a Presentation from MathWorks

Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit. In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and …

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“Three Key Factors for Successful AI Projects,” a Presentation from MathWorks

Bruce Tannenbaum, Technical Marketing Manager for AI applications at MathWorks, presents the "Three Key Factors for Successful AI Projects" tutorial at the May 2019 Embedded Vision Summit. AI is transforming the products we build and the way we do business. AI using images and video is already at work in our smart home devices, our …

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What Is Deep Learning? Three Things You Need to Know

This article was originally published at MathWorks' website. It is reprinted here with the permission of MathWorks. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to …

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Speeding Up Semantic Segmentation Using MATLAB Container from NVIDIA NGC

This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. Gone are the days of using a single GPU to train a deep learning model.  With computationally intensive algorithms such as semantic segmentation, a single GPU can take days to optimize a model. But multi-GPU hardware is expensive, …

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Multi-sensor Fusion for Robust Device Autonomy

While visible light image sensors may be the baseline "one sensor to rule them all" included in all autonomous system designs, they're not necessarily a sole panacea. By combining them with other sensor technologies: "Situational awareness" sensors; standard and high-resolution radar, LiDAR, infrared and UV, ultrasound and sonar, etc., and "Positional awareness" sensors such as …

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2018 Vision Product of the Year Award Winner Showcase: MathWorks (Software and Algorithms)

MathWorks' GPU Coder is the 2018 Vision Product of the Year Award Winner in the Software and Algorithms category. The new MathWorks® GPU Coder software enables scientists and engineers to automatically generate optimized CUDA code from high-level functional descriptions in MATLAB® for deep learning, embedded vision, and autonomous systems. The generated CUDA code, integrated in …

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Using MATLAB and TensorRT on NVIDIA GPUs

This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. As we design deep learning networks, how can we quickly prototype the complete algorithm—including pre- and postprocessing logic around deep neural networks (DNNs) —to get a sense of timing and performance on standalone GPUs? This question comes up …

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“Deep Learning in MATLAB: From Concept to Optimized Embedded Code,” a Presentation from MathWorks

Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, present the “Deep Learning in MATLAB: From Concept to Optimized Embedded Code” tutorial at the May 2018 Embedded Vision Summit. In this presentation, you’ll learn how to adopt MATLAB to design deep learning based vision applications and re-target …

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“How to Test and Validate an Automated Driving System,” a Presentation from MathWorks

Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from …

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May 18 - 21, Santa Clara, California

The preeminent event for practical, deployable computer vision and visual AI, for product creators who want to bring visual intelligence to products.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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