Overview
You will learn how to build on-Device AI Applications with JavaScript and deploy AI Applications to various devices!
Syllabus
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- Introduction to On-Device AI
-- Overview of AI and Machine Learning
-- Importance of On-Device AI
-- Benefits over Cloud-Based AI
- Foundations of On-Device AI Technology
-- Hardware and Software Requirements
-- Edge Devices: Smartphones, Tablets, and IoT Devices
-- Performance and Resource Constraints
- Designing On-Device AI Models
-- Model Selection for On-Device Applications
-- Techniques for Optimizing Model Performance
-- Model Compression and Quantization
- Development Tools and Frameworks
-- Introduction to TensorFlow Lite
-- Overview of Core ML for iOS
-- PyTorch Mobile Basics
- Implementing On-Device AI
-- Setting Up Development Environment
-- Building and Deploying AI Models
-- Integrating AI Models with Mobile Applications
- Real-Time Data Processing and Decision Making
-- Handling Data Locally on Devices
-- Real-Time Inference and Feedback
-- Privacy and Security Considerations
- Practical Applications and Use Cases
-- Image and Video Processing
-- Natural Language Processing on-Device
-- AR and VR Enhancements with AI
- Testing and Debugging On-Device AI Applications
-- Unit and Integration Testing
-- Performance Profiling and Monitoring
-- Addressing Common Issues and Bugs
- Future Trends and Developments
-- Advances in Edge Computing
-- Potential for AI in Emerging Technologies
-- The Evolving Landscape of Mobile AI
- Course Wrap-Up and Project Work
-- Recap of Key Concepts
-- Capstone Project: Building an On-Device AI Application
-- Resources for Continued Learning
Taught by
Tags
On-Demand
