Deep learning using Tensorflow Lite on Raspberry Pi

🚀 Course Title: Deep Learning using TensorFlow Lite on Raspberry Pi 🧠✈️

Course Headline: Power up your Embedded projects with Artificial Intelligence in Python using TF Lite

Course Workflow:

Embark on a journey to harness the power of Deep Learning on the Raspberry Pi 4, transforming it into an intelligent edge device. Throughout this course, you'll dive into hands-on projects with custom data, starting with approximating trigonometric functions and culminating in voice-controlled LEDs. 🎩✨

1. Trigonometric Functions Approximation: Generate random data to model and predict the Sin function using Python. This sets the foundation for understanding non-linear models. 📈

2. Visual Calculator: Create an application that takes image inputs, processes them through a Convolutional Neural Network (CNN) for categorical classification, and outputs mathematical results. 📷➡️🧮

3. Custom Voice-Controlled LEDs: Implement voice recognition to control LEDs. This project will introduce you to the intersection of AI, electronics, and hardware interaction using your own voice commands. 🎙️👉✨

4. Post Quantization & Model Optimization: Learn to apply Post Quantization techniques to TensorFlow models trained on Google Colab, reducing model size by up to 75% and speeding up inferencing to 0.03 seconds per input! 🔬⚡️

Sections:

1. Non-Linear Function Approximation
2. Visual Calculator
3. Custom Voice-Controlled Led

Outcomes After this Course:

• Develop Deep Learning Projects on Embedded Hardware 🛠️🧠
• Convert your models into Tensorflow Lite models for efficient deployment
• Speed up Inferencing on embedded devices, making your projects more responsive
• Master Post Quantization to optimize TensorFlow models
• Utilize custom data for AI projects to tailor the learning process
• Create Hardware Optimized Neural Networks that fit into IoT applications
• Implement Computer Vision projects using OPENCV and Tensorflow Lite
• Deploy Deep Neural Networks with fast inferencing speed 🚀

Hardware Requirements:

• Raspberry Pi 4 (the brain of our embedded AI system)
• 12V Power Bank (to power our projects on the go)
• 2 LEDs (Red and Green) for visual feedback
• Jumper Wires and Bread Board (for prototyping circuits)
• Raspberry Pi Camera V2 (for computer vision tasks)
• RPI 4 Fan (to keep our hardware cool during intensive processing)
• 3D printed parts (custom components for your projects)

Software Requirements:

• Python3 (our tool for coding and scripting)
• A motivated mind ready to tackle a massive programming project (your most important asset)

👩‍💻🧙‍♂️

Before buying, take a look into this course's GitHub repository! Get a glimpse of the projects, code snippets, and resources that will guide you through the course. This is your chance to see what you'll be building and learning 🛠️✨

Join us on this AI adventure with TensorFlow Lite on Raspberry Pi, where cutting-edge technology meets practical application! 🎉🚀