AI on a Pi

Overview

Delve into the world of deep learning with our "AI on a Pi" event. This unique session offers an in-depth exploration with Python and MXNet, particularly tailored for applications on the Raspberry Pi.

Whether your interest lies in computer vision or natural language processing, you'll gain practical insights and demonstrations designed to enhance your knowledge and skills in artificial intelligence.

Presented by experts in the field, our session combines theory and hands-on practice, providing a comprehensive understanding of deploying AI on compact hardware. Do not miss this opportunity to expand your AI expertise with cutting-edge techniques on accessible, affordable technology.

Perfect for enthusiasts and professionals eager to advance their capabilities in AI development.

Syllabus

• Introduction to AI and Deep Learning
Overview of Artificial Intelligence and its applications
Basics of Deep Learning and neural networks
Introduction to MXNet and its ecosystem
• Getting Started with Raspberry Pi
Setting up Raspberry Pi: installation and configuration
Installing Python and necessary libraries on Raspberry Pi
Introduction to GPIO and Raspberry Pi hardware capabilities
• Python for Deep Learning
Python refresher: syntax, libraries, and best practices
Using NumPy and Pandas for data manipulation
Overview of popular deep learning libraries: MXNet, TensorFlow, and PyTorch
• Deep Learning with MXNet
Setting up MXNet on Raspberry Pi
Understanding data loading and preprocessing
Building and training neural networks with MXNet
Implementing Convolutional Neural Networks (CNNs)
• Computer Vision on Raspberry Pi
Introduction to computer vision and its applications
Setting up and using the Pi Camera module
Implementing image classification tasks with MXNet and Raspberry Pi
Real-time object detection on Raspberry Pi
• Natural Language Processing (NLP) on Raspberry Pi
Overview of NLP tasks and applications
Tools and libraries for NLP with MXNet
Implementing text classification and sentiment analysis
Real-time language translation on Raspberry Pi
• Optimization and Deployment
Optimizing deep learning models for Raspberry Pi
Techniques for performance improvements: quantization and pruning
Deployment strategies for edge devices
• Project: Building an AI-powered Application
Define a project scope combining computer vision and NLP
Implementing the project on Raspberry Pi
Testing and evaluation of the AI application
• Future Trends and Conclusion
Discussing future trends in AI and edge computing
Course wrap-up and further learning resources

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Subjects

Conference Talks