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Deep learning using Tensorflow Lite on Raspberry Pi

Deep learning using Tensorflow Lite on Raspberry Pi

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

What you’ll learn

Deep learning using Tensorflow Lite on Raspberry Pi

  • Build your own AI Projects

  • Raspberry Pi 4-based Robot for Computer Vision

  • Neural Network to classify your Voice

  • Custom Convolution Network Creation


  • Basic Electronics Understanding

  • Basic Python Programming

  • Hardware: Raspberry pi 4

  • Hardware : 12V Power Bank

  • Hardware: Raspberry PI Camera V2

  • Hardware: 2 LEDs ( Red and Green )

  • Hardware: Bread Board

  • Hardware: RPI 4 Fan

  • Hardware : 3D printed Parts

  • Hardware: Jumper Wires


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


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


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

Who this course is for:

  • Developers
  • Electrical Engineers
  • Artificial Intelligence Enthusiasts

Deep learning using Tensorflow Lite on Raspberry Pi

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