Intel Software AI Training

This training aim to Learn AI theory and follow hands-on exercises with  free courses from the Intel® AI Academy for software developers, data scientists, and students. These lessons cover AI topics and explore tools and optimized libraries that take advantage of Intel® processors in personal computers and server workstations.

Product availability: AGAINST PO

FEATURES

  • Learn how to use Intel® hardware, software, and solutions for AI on the PC.

    By the end of this Training will have practical knowledge of:

    • Windows* Machine Learning to accelerate machine learning applications
    • The Model Optimizer and inference engine in the Intel® Distribution for OpenVINO™ toolkit on multiple types of hardware
    • Deep learning tools and frameworks, such as TensorFlow* and Open Neural Network Exchange (ONNX*)

Advanced Training Section Divided into 8 Section:

Module 1

This class introduces the basics of AI:

  • Applications of AI and ways it can transform industries
  • Comparison between machine learning and deep learning
  • Basic deep learning terminology

Module 2

This class reviews how Intel hardware is used for AI. Topics include:

  • Intel’s vision for AI on PC hardware and software
  • How different hardware addresses various AI tasks, such as training and inference
  • The analytics ecosystem, which is made up of toolkits, libraries, solutions, and hardware

Module 3

This class teaches about deep learning frameworks and provides:

  • An overview of the optimized frameworks for machine learning
  • An introduction to TensorFlow and central concepts, such as computational graphs and sessions
  • Instructions to create and run a simple computational graph in Python*

Module 4

This class explains the end-to-end AI training workflow. Topics include:

  • How to clean, normalize, and optimize a dataset
  • An example of how to train a GoogLeNet Inception neural network model
  • How to evaluate a trained model and test it for accuracy and performance

Module 5

This class introduces the challenges of AI inference at the edge. Topics include:

  • What edge computing is and how it will influence modern technology
  • The importance of inference on the edge and why it’s required by emerging markets

Module 6

This class introduces how to use Windows Machine Learning to accelerate AI development. Topics include:

  • The benefits of using Windows Machine Learning for inference on the edge
  • How to improve performance using the most popular frameworks with ONNX models
  • How the Windows Machine Learning stack can improve performance of AI models on integrated graphics

Module 7

This class introduces the Intel® Distribution of OpenVINO™ toolkit and how to use it to run inference on the edge. Learn about:

  • The different parts and advantages of using the toolkit
  • How to use the Model Optimizer to improve the model topology of pretrained networks
  • How to use the inference engine to run on different types of hardware

Module 8

Complete this course with a review of the previous topics, including:

  • How Intel hardware, toolkits, and solutions allow developers to create applications for AI on the PC
  • Why Intel’s collaboration with Microsoft* improves deep learning performance for PCs through Windows Machine Learning
  • An introduction to Intel® Distribution of OpenVINO™ toolkit to use with deep learning frameworks for powerful AI applications