Evaluating Current and Future AI Technologies and Frameworks

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description

Solid knowledge of the AI technology landscape is fundamental in choosing the right tools to use as an AI Architect. In this course, you'll explore the current and future AI technology landscape, comparing the advantages and disadvantages of common AI platforms and frameworks.

You'll move on to examine AI libraries and pre-trained models, distinguishing their advantages and disadvantages. You'll then classify AI datasets and see a list of dataset topics. Finally, You'll learn how to make informed decisions about which AI technology is best suited to your projects.

Expected Duration (hours)
0.7

Lesson Objectives

Evaluating Current and Future AI Technologies and Frameworks

discover the key concepts covered in this course

compare and contrast AI platforms, frameworks, libraries, pre-trained models, and datasets

recognize the key features and advantages/disadvantages of common AI platforms

identify the TensorFlow framework and distinguish its advantages/disadvantages

identify the Keras framework and distinguish its advantages/disadvantages

identify the PyTorch framework and distinguish its advantages/disadvantages

identify the MXNet framework and distinguish its advantages/disadvantages

identify the CNTK framework and distinguish its advantages/disadvantages

identify the Cortex framework and explore its key features

examine AI libraries and identify their advantages/disadvantages

recognize pre-trained models and distinguish their advantages/disadvantages

classify AI datasets and recognize a list of dataset topics

summarize the key concepts covered in this course

Course Number:
it_aievaldj_01_enus

Expertise Level
Intermediate