�ݺ�ߣshows by User: LucasGarcaRodrguez

�ݺ�ߣshows by User: LucasGarcaRodrguez / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: LucasGarcaRodrguez / Fri, 29 Apr 2022 15:00:51 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: LucasGarcaRodrguez How to train your robot (with Deep Reinforcement Learning) /slideshow/how-to-train-your-robot-with-deep-reinforcement-learning/251692948 howtotrainyourrobotwithdrl-220429150052
The talk addresses the full workflow for Deep Reinforcement Learning: choosing an adequate environment, crafting a reward function, choosing a policy function, training and deployment. Using Model-Based Design, the talk demonstrates how to build and control a virtual biped humanoid robot in Simulink and leverages Deep Reinforcement Learning in MATLAB, specifically the Deep Deterministic Policy Gradient (DDPG), to successfully train the agent. Finally, we discuss how to deploy the optimal policies to the target hardware, using C/C++ or CUDA.]]>
The talk addresses the full workflow for Deep Reinforcement Learning: choosing an adequate environment, crafting a reward function, choosing a policy function, training and deployment. Using Model-Based Design, the talk demonstrates how to build and control a virtual biped humanoid robot in Simulink and leverages Deep Reinforcement Learning in MATLAB, specifically the Deep Deterministic Policy Gradient (DDPG), to successfully train the agent. Finally, we discuss how to deploy the optimal policies to the target hardware, using C/C++ or CUDA.]]> Fri, 29 Apr 2022 15:00:51 GMT /slideshow/how-to-train-your-robot-with-deep-reinforcement-learning/251692948 LucasGarcaRodrguez@slideshare.net(LucasGarcaRodrguez) How to train your robot (with Deep Reinforcement Learning) LucasGarcaRodrguez The talk addresses the full workflow for Deep Reinforcement Learning: choosing an adequate environment, crafting a reward function, choosing a policy function, training and deployment. Using Model-Based Design, the talk demonstrates how to build and control a virtual biped humanoid robot in Simulink and leverages Deep Reinforcement Learning in MATLAB, specifically the Deep Deterministic Policy Gradient (DDPG), to successfully train the agent. Finally, we discuss how to deploy the optimal policies to the target hardware, using C/C++ or CUDA. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtotrainyourrobotwithdrl-220429150052-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> The talk addresses the full workflow for Deep Reinforcement Learning: choosing an adequate environment, crafting a reward function, choosing a policy function, training and deployment. Using Model-Based Design, the talk demonstrates how to build and control a virtual biped humanoid robot in Simulink and leverages Deep Reinforcement Learning in MATLAB, specifically the Deep Deterministic Policy Gradient (DDPG), to successfully train the agent. Finally, we discuss how to deploy the optimal policies to the target hardware, using C/C++ or CUDA.

How to train your robot (with Deep Reinforcement Learning) from Lucas Garc鱈a, PhD

]]> 44279 0 https://cdn.slidesharecdn.com/ss_thumbnails/howtotrainyourrobotwithdrl-220429150052-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Deep Learning and the technology behind Self-Driving Cars /slideshow/deep-learning-and-the-technology-behind-selfdriving-cars/232361503 deeplearningselfdriving-200421115545
Self-driving cars, voice assistants, autonomous robots, smart devices… Autonomous systems are reaching to and changing every part of our lives, and Deep Learning is the technology behind that change. Advanced levels of perception, enabled by Deep Learning, are key to the success of automated driving, from advanced driver assistance systems (ADAS) to fully autonomous driving. Designing and deploying deep learning applications to embedded CPU and GPU platforms (as it is the case with cars) is challenging because of resource constraints inherent in embedded devices. In this session, you will be exposed to some of the most relevant and stimulating real-world problems in ADAS, focusing on the role played by Deep Neural Networks (DNN): image classification, object detection and localization, semantic segmentation and deployment. You will get a walkthrough of a complete workflow including data preparation (properly ground-truth labeling of datasets and driving scenes) and visualization, creating or fine-tuning DNNs, training such networks leveraging NVIDIA GPUs to build automated driving capabilities and generating portable and optimized CUDA code that can be deployed on boards (NVIDIA Jetson TX2 and NVIDIA DRIVE PX) leveraging TensorRT for very fast inference. Video: https://youtu.be/Oal-ac7QbkE]]>
Self-driving cars, voice assistants, autonomous robots, smart devices… Autonomous systems are reaching to and changing every part of our lives, and Deep Learning is the technology behind that change. Advanced levels of perception, enabled by Deep Learning, are key to the success of automated driving, from advanced driver assistance systems (ADAS) to fully autonomous driving. Designing and deploying deep learning applications to embedded CPU and GPU platforms (as it is the case with cars) is challenging because of resource constraints inherent in embedded devices. In this session, you will be exposed to some of the most relevant and stimulating real-world problems in ADAS, focusing on the role played by Deep Neural Networks (DNN): image classification, object detection and localization, semantic segmentation and deployment. You will get a walkthrough of a complete workflow including data preparation (properly ground-truth labeling of datasets and driving scenes) and visualization, creating or fine-tuning DNNs, training such networks leveraging NVIDIA GPUs to build automated driving capabilities and generating portable and optimized CUDA code that can be deployed on boards (NVIDIA Jetson TX2 and NVIDIA DRIVE PX) leveraging TensorRT for very fast inference. Video: https://youtu.be/Oal-ac7QbkE]]> Tue, 21 Apr 2020 11:55:45 GMT /slideshow/deep-learning-and-the-technology-behind-selfdriving-cars/232361503 LucasGarcaRodrguez@slideshare.net(LucasGarcaRodrguez) Deep Learning and the technology behind Self-Driving Cars LucasGarcaRodrguez Self-driving cars, voice assistants, autonomous robots, smart devices… Autonomous systems are reaching to and changing every part of our lives, and Deep Learning is the technology behind that change. Advanced levels of perception, enabled by Deep Learning, are key to the success of automated driving, from advanced driver assistance systems (ADAS) to fully autonomous driving. Designing and deploying deep learning applications to embedded CPU and GPU platforms (as it is the case with cars) is challenging because of resource constraints inherent in embedded devices. In this session, you will be exposed to some of the most relevant and stimulating real-world problems in ADAS, focusing on the role played by Deep Neural Networks (DNN): image classification, object detection and localization, semantic segmentation and deployment. You will get a walkthrough of a complete workflow including data preparation (properly ground-truth labeling of datasets and driving scenes) and visualization, creating or fine-tuning DNNs, training such networks leveraging NVIDIA GPUs to build automated driving capabilities and generating portable and optimized CUDA code that can be deployed on boards (NVIDIA Jetson TX2 and NVIDIA DRIVE PX) leveraging TensorRT for very fast inference. Video: https://youtu.be/Oal-ac7QbkE <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningselfdriving-200421115545-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Self-driving cars, voice assistants, autonomous robots, smart devices… Autonomous systems are reaching to and changing every part of our lives, and Deep Learning is the technology behind that change. Advanced levels of perception, enabled by Deep Learning, are key to the success of automated driving, from advanced driver assistance systems (ADAS) to fully autonomous driving. Designing and deploying deep learning applications to embedded CPU and GPU platforms (as it is the case with cars) is challenging because of resource constraints inherent in embedded devices. In this session, you will be exposed to some of the most relevant and stimulating real-world problems in ADAS, focusing on the role played by Deep Neural Networks (DNN): image classification, object detection and localization, semantic segmentation and deployment. You will get a walkthrough of a complete workflow including data preparation (properly ground-truth labeling of datasets and driving scenes) and visualization, creating or fine-tuning DNNs, training such networks leveraging NVIDIA GPUs to build automated driving capabilities and generating portable and optimized CUDA code that can be deployed on boards (NVIDIA Jetson TX2 and NVIDIA DRIVE PX) leveraging TensorRT for very fast inference. Video: https://youtu.be/Oal-ac7QbkE

Deep Learning and the technology behind Self-Driving Cars from Lucas Garc鱈a, PhD

]]> 654 1 https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningselfdriving-200421115545-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/howtotrainyourrobotwithdrl-220429150052-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-to-train-your-robot-with-deep-reinforcement-learning/251692948 How to train your robo... https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningselfdriving-200421115545-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deep-learning-and-the-technology-behind-selfdriving-cars/232361503 Deep Learning and the ...