AI on a Pi

Overview

Explore Deep Learning with Python and MXNet, including practical demonstrations on a Raspberry Pi for computer vision and natural language processing tasks.

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