->
Deep learning using Tensorflow Lite on Raspberry Pi
Deep learning using Tensorflow Lite on Raspberry Pi
https://www.udemy.com/course/deep-learning-using-tensorflow-lite-on-raspberry-pi/
Power up your Embedded projects with Artificial Intelligence in Python using TF Lite

 


Course Updated ROS Kinetic to ROS 2 Foxy :

Rating is for OLD version of this course , New update to projects and way of explanation is what you are going to love :)

Course Workflow:

This Course is for mobile robot which is a 2 wheel differential drive with a caster . We will First build the robot using 3D printed parts. All electronics is going to be explained for proper connections .

Raspberry Pi 4 is going to be main brain for this robot . ROS2 foxy and humble both are going to be utilized using this course . WiFi Communication between laptop and Raspberry Pi will be done .

We will look into image data transmission  and bandwidth optimization for our computer vision based projects . 

 

 

Sections  :

  1. ROS2 Workspace Raspberry pi Setup

  2. Robot Building and Driving with Joystick

  3. QR Maze Solving using OpenCV

  4. Line Following Real and Simulation Robot

  5. AI Surveillance Robot using Tensorflow Lite

Outcomes After this Course : You can create

  • Custom Workspace

  • Custom Python Packages

  • Launch files

  • Custom Mobile Robots

  • ROS 2 Robot and Simulation integration

  • RVIZ and Gazebo Simulation Fundamentals

  • Computer Vision with ROS 2 using OPENCV

  • Deep Neural Networks on ROS 2 based Nodes

 

Software Requirements

  • Ubuntu 22.04

  • ROS 2 Foxy

  • Motivated mind for a huge programming Project

Deep learning using Tensorflow Lite on Raspberry Pi


 TO MAC USERS: If RAR password doesn't work, use this archive program: 

RAR Expander 0.8.5 Beta 4  and extract password protected files without error.


 TO WIN USERS: If RAR password doesn't work, use this archive program: 

Latest Winrar  and extract password protected files without error.


 Gamystyle   |  

Information
Members of Guests cannot leave comments.




rss