This Deep Learning for Intelligent Video Analytics training course teaches you the foundational elements of intelligent video analytics (IVA) by integrating deep learning models and algorithms into a ready-to-use NVIDIA DeepStream pipeline. First, you will gain an in-depth understanding of data normalization, annotation, and metadata formatting in IVA applications, followed by a fundamental understanding of object detection in video frames. You will proceed to train object tracking models and learn how to utilize temporal features of videos to achieve more efficient detection models. You will also learn to deploy and accelerate the development of your IVA application by plugging deep neural networks into an end-to-end stream processing pipeline using the DeepStream framework. Throughout the workshop, you will get hands-on coding experience using a live GPU-accelerated environment to deploy and train your models. At the end, you will have access to additional resources to design and deploy IVA applications on your own.
By attending Deep Learning for Intelligent Video Analytics workshop, delegates will:
- Deploy deep learning models for accurate and effective object detection and tracking applications
- Accelerate the development of IVA applications by using the DeepStream framework
- 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, healthcare, and more
- Benefit from guided hands-on experience using the widely-used, industry-standard software, tools, and frameworks
- Gain real world expertise through content designed in collaboration with industry leaders such as the Children's Hospital Los Angeles, Mayo Clinic, and PwC
- Earn a DLI certificate to demonstrate your subject matter competency and support professional career growth
- Access content anywhere, anytime with a fully-configured, GPU-accelerated workstation in the cloud
- Experience with deep neural networks (specifically variations of CNNs) and intermediate-level experience with C and Python