DLI Training: Deep Learning for Autonomous Vehicles

The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to optimize performance for self-driving car perception…

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NVIDIA
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Nefi Alarcon
•2 min read•intermediate•
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Deep LearningKeras

Overview

The article discusses the transformative impact of AI on the automotive industry, particularly through the development of autonomous driving technology. It highlights the NVIDIA Deep Learning Institute's (DLI) training program that equips participants with the skills to optimize self-driving car perception applications.

What You'll Learn

1

How to build and train a semantic segmentation neural network for object identification in autonomous vehicles

2

How to deploy a TensorRT model on the NVIDIA DRIVE™ AGX platform for practical applications

3

How to run example code using different GPU memory configurations on the DRIVE AGX platform

4

Why performance optimization methods using CUDA are essential for self-driving applications

Key Questions Answered

What skills will participants gain from the NVIDIA DLI training for autonomous vehicles?
Participants will learn to build and train a semantic segmentation neural network, deploy TensorRT models, and optimize performance using CUDA on the NVIDIA DRIVE™ AGX platform. This training prepares them for various autonomous driving scenarios, enhancing their competencies in AI applications.
What technologies are used in the development of autonomous driving systems?
Autonomous driving systems utilize camera-based machine vision, radar, and lidar-based detection units to perceive and navigate environments. These technologies are crucial for ensuring safe and efficient self-driving capabilities.
How does the NVIDIA DLI training enhance career growth in AI for autonomous vehicles?
By completing the NVIDIA DLI training, participants earn a certificate that demonstrates their competency in AI applications for autonomous vehicles, which can significantly accelerate their career growth in a rapidly evolving industry.

Key Statistics & Figures

Projected market value of AI-powered robotaxis
$2 trillion
This value is expected to be reached worldwide by 2030, indicating the significant economic impact of AI in the automotive sector.

Technologies & Tools

Hardware
Nvidia Drive™ Agx
Used as the platform for deploying and running AI applications for autonomous vehicles.
Software
Cuda
Utilized for performance optimization in self-driving applications.
Software
Tensorrt
Used for optimizing and deploying neural network models in practical applications.
Software
Digits
Tool used for training semantic segmentation models with Cityscapes data.

Key Actionable Insights

1
Engaging in the NVIDIA DLI training can significantly enhance your skills in AI applications for autonomous vehicles.
This training provides hands-on experience with cutting-edge technologies, making it a valuable opportunity for software engineers looking to specialize in autonomous driving.
2
Optimizing GPU memory configurations is critical for maximizing performance in self-driving applications.
Understanding how different configurations affect performance can lead to more efficient deployments and better real-time processing capabilities.
3
Learning to convert Keras and TensorFlow models into optimized TensorRT models is essential for practical applications.
This skill allows developers to leverage the full potential of NVIDIA's hardware, ensuring that their models run efficiently in real-world scenarios.