Introduction to Intel® Distribution of OpenVINO™ Toolkit
Course Description
This course introduces the AI algorithms and framework in the Intel® Distribution of OpenVINO™ toolkit, which is used to solve complex problems.
- This toolkit is a suite of tools for performing optimizations and inference on trained deep learning models into Python*, C, and C++ applications, and deploying these applications to the edge, network edge, or cloud.
- It provides acceleration on Intel® CPUs, GPUs, VPUs, and other Intel® hardware architecture accelerators.
- The OpenVINO toolkit is compatible with many common libraries such as TensorFlow*, PyTorch*, ONNX* (Open Neural Network Exchange), and many others, and provides increased performance above stock libraries on Intel® architecture.
Included in this course:
- 5 modules (Estimated time to complete: 31 hours)
- 9 lab exercises
Modules
Introduction to AI and the Intel Distribution of OpenVINO™ Toolkit
Optimization and Quantization of AI Models for Improved Performance
Create Scalable and Future-Ready AI Applications with the Inference Engine
Hardware Accelerators for Deep Learning
Streamline AI Application Development with the Deep Learning Workbench
Get the Assignments and Quizzes
Five modules and nine lab exercises with slide presentations and quizzes are available as a separate download.
Details
Learning Objectives
After completing this course, students will be able to:
- Analyze and optimize deep learning models for computer vision, natural language processing, and more.
- Describe and program the OpenVINO API into applications to run deep learning inference.
- Deploy inference for deep learning models heterogeneously.
- Describe where to download and install the OpenVINO toolkit.
Target Audience
Senior undergraduate and graduate students studying:
- Computer science
- Engineering
- Science and mathematics
Prerequisites
- Python* programming at a minimum; C or C++ programming is beneficial
- Basic understanding of neural network trained models, and their weights and biases