These are my personal notes taken while following the Udacity Computer Vision Nanodegree.

The nanodegree is composed of these modules:

1. Introduction to Computer Vision
2. Cloud Computing (Optional)
3. Advanced Computer Vision and Deep Learning
4. Object Tracking and Localization

Each module has a folder with its respective notes; you need to go to each module folder and follow the Markdown file in them.

Additionally, note that:

• I have also notes on the Udacity Deep Learning Nanodegree in my repository deep_learning_udacity; that MOOC is strongly related and has complementary material. I have many hand-written notes in deep_learning_udacity related to this repository, too.
• The exercises are commented in the Markdown files and linked to their location; most of the exercises are located in other repositories, originally forked from Udacity and extended/completed by me:
  • CVND_Exercises
  • DL_PyTorch
  • CVND_Localization_Exercises
  • deep-learning-v2-pytorch

Udacity requires the submission of a project for each module; these are the repositories of the projects I submitted:

1. Facial Keypoint Detection with Deep Convolutional Neural Networks (CNNs): P1_Facial_Keypoints.
2. Image Captioning: Image Description Text Generator Combining CNNs and RNNs: image_captioning.
3. Landmark Detection & Tracking (SLAM): slam_2d.

You need to follow the installation & setup guide from CVND_Exercises, which can be summarized with the following commands:

# Create new conda environment to be used for the nanodegree
conda create -n cvnd python=3.6
conda activate cvnd
conda install pytorch torchvision -c pytorch
conda install pip

# Go to the folder where the Udacity DL exercises are cloned/forked,
# after forking the original repo
cd ~/git_repositories/CVND_Exercises
pip install -r requirements.txt

Mikel Sagardia, 2022.
No guarantees.

If you find this repository helpful and use it, please link to the original source.