afondiel / Self-Driving-Cars-Perception-and-Deep-Learning-Free-Course-freeCodeCamp

Notes and key takeaways of the Self-Driving Cars Perception applied Deep Learning Free Course from freeCodeCamp.org

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Self-Driving Cars Perception and Deep Learning - Free Course from freeCodeCamp.org

Overview

Notes and key takeaways of the Self-Driving Cars Perception and Deep Learning Free Course from freeCodeCamp.org.

Learn some computer vision core tasks for self-driving cars perception stack using deep learning, including road segmentation, object detection and tracking, and 3D data visualization.

Prerequisites

  • ~2 hours
  • Free course (No certificate awarded)
  • Intermediate level: Python and deep learning (required)

Instructor

Course Objectives

  • Gain a comprehensive understanding of the key computer vision tasks involved in self-driving car perception
  • Deepen knowledge of deep learning techniques, particularly Convolutional Neural Networks (CNNs), for addressing these tasks
  • Learn how to implement and evaluate deep learning models for road segmentation, object detection, tracking, and camera to bird's eye view (BEV) mapping
  • Acquire hands-on experience with real-world data and popular deep learning libraries

Course Contents

Read the Courses Online

Read the courses Online, you can visit Perception for Self-Driving Cars Applied Deep Learning.

Lab - Notebooks

Chapter Exercises
1. Road Segmentation - Fully Convolutional Network (FCN) Open notebook in Colab Kaggle
2. 2D Object Detection - YOLO Open notebook in Colab Kaggle
3. Object Tracking - Deep SORT Open notebook in Colab Kaggle
4. 3D Data Visualization - Homogenous Transformations - KITTI Open notebook in Colab Kaggle
5. Multi Task Learning - Multi Task Attention Network (MTAN) Open notebook in Colab Kaggle
6. 3D Object Detection - SFA 3D Open notebook in Colab Kaggle
7. Camera to Bird's Eye View - UNetXST Open notebook in Colab Kaggle

Happy Learning!

Contributing

  • Feel free to open a PR or an issue if you encounter some problem running the projects.

Here's how you can contribute:

  • Fork this repository and clone it to your local machine.
  • Create a new branch with a descriptive name for your contribution.
  • Add your code and files to the branch and commit your changes.
  • Push your branch to your forked repository and create a pull request to the main repository.
  • Wait for your pull request to be reviewed and merged.

References

Full course link:

Other related courses:

Zero (Beginner):

Apprentice (Intermediate):

Hero (Advanced):

"Vision is a picture of the future that produces passion." ~ Bill Hybels