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