Overview
Master how to build on-Device AI Applications and deploy AI Applications into various devices!
Syllabus
-
- Introduction to On-Device AI
-- Overview of AI deployment methods
-- Benefits of on-device AI vs. cloud-based AI
-- Real-world applications of on-device AI
- Foundations of On-Device AI
-- Introduction to machine learning and AI concepts
-- Overview of neural networks and deep learning
-- Basic architecture of on-device AI systems
- Hardware for On-Device AI
-- Types of devices (smartphones, wearables, IoT sensors)
-- Key hardware components (CPUs, GPUs, NPUs)
-- Power and energy considerations
- Software for On-Device AI
-- Overview of operating systems and platforms
-- Introduction to machine learning libraries and frameworks
-- Tools for model optimization and deployment
- Data Management and Processing
-- Data collection and preprocessing on-device
-- Handling data privacy and security
-- Techniques for data compression and management
- Building On-Device AI Models
-- Model selection for on-device applications
-- Techniques for model training and optimization
-- Approaches to model deployment and updating
- Case Studies and Industry Applications
-- In-depth analysis of existing on-device AI applications
-- Success stories from various industries
-- Challenges and solutions in deploying on-device AI
- Future Trends and Developments
-- The role of 5G and edge computing
-- Advances in hardware and software for on-device AI
-- Emerging use cases and opportunities
- Hands-on Project
-- Designing and implementing a simple on-device AI application
-- Optimizing AI models for target devices
-- Testing and deployment of the application
- Course Wrap-Up
-- Review of key concepts and skills
-- Discussion of industry impact and future directions
-- Resources for further learning and development
Taught by
Tags
On-Demand
