From the Data Center to the Edge –An Optimized Path using Intel® Architecture Course

Summary:

Whether you are an experienced data scientist or new to the field, this course takes a hands-on approach in helping you understand the data science workflow, how to 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.

Upon completion, you have the option to receive an Intel® AI Course Completion Certificate**

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 the Intel 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 TensorFlow* with Intel optimizations
Deploying on the CPU, Integrated Graphics and Intel® Neural Compute Stick 2 (Intel® NCS2) using the Intel® Distribution of OpenVINO™ Toolkit

Prerequisites:

Prior to the course, you should have:

A basic understanding of AI principles, machine learning, and deep learning
Coding experience with Python*
Some exposure to different frameworks – TensorFlow*, Caffe*, etc.
Optional introduction courses:

The estimated time to complete this course is 4-5 hours**

**Course does not have to be completed in one session.

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