Deep learning using Tensorflow Lite on Raspberry Pi

Course Workflow:

This course is focused on Embedded Deep learning in Python. Raspberry PI 4 is utilized as the main hardware and we will be building practical projects with custom data.

We will start with trigonometric function approximation. In which we will generate random data and produce a model for the Sin function approximation

Next is a calculator that takes images as input and builds up an equation and produces a result. This Computer vision based project is going to be using convolution network architecture for Categorical classification

Another amazing project is focused on a convolution network but the data is custom voice recordings. We will involve a few electronics to show the output by controlling our multiple LEDs using our own voice.

A unique learning point in this course is Post Quantization applied to Tensor flow models trained on Google Colab. Reducing the size of models to 3 times and increasing inferencing speed up to 0.03 sec per input.

Sections  :

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

Outcomes After this Course: You can create

• Deep Learning Projects on Embedded Hardware
• Convert your models into Tensorflow Lite models
• Speed up Inferencing on embedded devices
• Post Quantization
• Custom Data for Ai Projects
• Hardware Optimized Neural Networks
• Computer Vision projects with OpenCV
• Deep Neural Networks with fast inferencing Speed

Hardware Requirements

• Raspberry PI 4
• 12V Power Bank
• 2 LEDs ( Red and Green )
• Jumper Wires
• Bread Board
• Raspberry PI Camera V2
• RPI 4 Fan
• 3D printed Parts

Software Requirements

• Python3
• Motivated mind for a huge programming Project

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  Before buying take a look at this course’s GitHub repository