Deep Learning Architectures for Defense Essentials from Tonex, Inc.

Deep Learning Architectures for Defense Essentials

"Deep Learning Architectures for Defense Essentials" introduces military applications of deep learning, including image recognition, signal classification, and autonomous systems. The course emphasizes securing model pipelines, training data, and inference layers against adversarial inputs and model theft. Cybersecurity integration ensures that defense AI systems remain mission-capable, tamper-resistant, and protected from reverse engineering or operational compromise in hostile environments.

Course Overview

Overall Proficiency Level

2 - Intermediate

Course Catalog Number

T101

Course Prerequisites

None

Training Purpose

Functional Development

Management Development

Specific Audience

All

Delivery Method

Online, Instructor-Led

Online, Instructor-Led

Learning Objectives

Understand the fundamental deep learning architectures used in defense applications.
Analyze the strengths and weaknesses of different deep learning models.
Explore the applications of deep learning in areas like image recognition and threat detection.
Learn about the hardware and software considerations for deploying deep learning models.
Understanding these architectures helps in identifying potential vulnerabilities and designing more secure defense systems leveraging AI.

Framework Connections

Design and Development

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Competency Areas

Work Roles

Feedback

If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.