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*)
SPECIFICATIONS
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