AI from the Data Center to the Edge
An Optimized Path Using Intel® Architecture
Summary
Whether you are an experienced data scientist or new to the field, this course takes a hands-on approach to help you understand the data science workflow, use Intel’s AI portfolio of processors and optimized software, and apply them to the challenge you are facing. The course uses an enterprise image classification problem and provides lectures for each stage in the process, accompanied by Jupyter* notebooks that walk you through the implementation.
Topics include:
- An overview of Intel’s AI portfolio with an emphasis on solving deep learning problems
- Dataset preparation for model consumption–including preprocessing and data augmentation techniques
- Decision metrics for choosing a framework and network (topology)
- How to train and deploy deep learning models using Intel's AI portfolio
By the end of this course, you will have practical knowledge of:
- Preparing a dataset for model consumption
- Training a deep learning model using Intel® Optimization for TensorFlow*
- Deploying on the CPU, integrated graphics, and Intel® Neural Compute Stick 2 (Intel® NCS2) using the Intel® Distribution of OpenVINO™ toolkit
The estimated time to complete this course is five hours.
Tools and frameworks used in this course are part of AI Tools from Intel.
- TensorFlow
- Intel Distribution of OpenVINO toolkit
Learn more about oneAPI.
Prerequisites
A basic understanding of AI principles, machine learning, and deep learning
Python* programming
Some exposure to different frameworks, such as TensorFlow and Caffe*
Optional introduction courses: