This workshop teaches you to apply deep learning techniques to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE development platform through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows by
performing neural network training on a fully-configured GPU accelerated workstation in the cloud. The workshop starts with an introduction of Sensor Abstraction Layer (SAL) which is required for software to interface with the hardware sensors. After covering SAL and concepts on DRIVE PX, we teach you the
steps required to do semantic segmentation on DRIVE PX and conclude by teaching techniques to leverage TensorRT, a high-performance neural network inference engine for production deployment of deep learning applications, to optimize, validate, and deploy trained neural network for inference in a
self-driving car application.
Experience with CNNs
Experience with CNNs
At the conclusion of the workshop, you will have an understanding of:
Integrating sensor inputs using the DriveWorks software stack
Training a semantic segmentation neural network
Optimizing, validatating, and deploying a trained neural network using TensorRT
Why Deep Learning Institute Hands-on Training?
Learn how to build deep learning and accelerated computing applications across a wide range of industry segments such as Autonomous Vehicles, Digital Content Creation, Finance, Game Development, and Healthcare.
Obtain guided hands-on experience using the most widely used, industry-standard software, tools, and frameworks
Attain real world expertise through content designed in collaboration with industry leaders such as the Children’s Hospital of Los Angeles, Mayo Clinic, and PwC
Earn NVIDIA DLI Certification to prove your subject matter competency and support professional career growth
Access content anywhere, anytime with a fully configured GPU-accelerated workstation in the cloud
Instructor introduction and environment setup
Drive PX Overview
Overview of NVIDIA DriveWorks and DRIVE PX platform
Learn about the capabilities of DRIVE Platform
Building Autonomous Vehicles with DRIVE PX
Interface with sensors using Sensor Abstraction Layer on DRIVE PX2
Use the tools and Labs to do perception on the vehicle
Integrate DriveWorks Labs into custom code or applications
Learn how to use DriveWorks into custom code or applications.
Training Semantic Segmentation for DRIVE PX
Convert an existing network into a fully convolutional network
Explore different design choices to fit into the computation budget
Train a semantic segmentation neural network
Learn how to leverage computation capabilities of DRIVE PX2 to do semantic segmentation.
Deployment of Semantic Segmentation Network Using TensorRT
Profile inference performance using Drive PX2
Optimize using giexec or own executable
Deep dive into INT8 calibration workflow
Learn how to use TensorRT to optimize, validate, and deploy trained neural network for inference in a self-driving car application.
Closing Comments & Questions
Wrap-up with the potential next steps and Q&A
Quick overview of the next -steps you could leverage to build and deploy your own applications and any Q&A.
Tools, Libraries, and Frameworks: TensorFlow,DIGITS,TensorRT
Deep Learning for Autonomous Vehicles—Perception Eğitimi hakkında ilginizi çekebilecek yazılar