Merge branch 'johko:main' into TableOfContent

README.md

@@ -139,7 +139,8 @@ If you want to refer to a section inside the text, you can do it like below 👇
 You can write codeblocks by wrapping it with three backticks. Please add the associated language code, e.g. `py` or `bash` after top backticks to enable language specific rendering of code blocks. 
 
 **LaTeX**
-You can write LaTeX by writing it like this: `$$...$$``
+You can write in-line LaTeX by writing it like this: ` \\( X )\\`
+You can write stand alone LaTeX by enclosing with  `$$`.
 For example 👇 
 ```
 $$Y = X * \textbf{dequantize}(W); \text{quantize}(W)$$
@@ -168,7 +169,7 @@ $$Y = X * \textbf{dequantize}(W); \text{quantize}(W)$$
 - If you have any images, videos and more in your PRs, please store them in [this Hugging Face repository](https://huggingface.co/datasets/hf-vision/course-assets) to keep this repository lightweight. You can ask for an access to the organization if you aren't a part of it yet. The steps to do so are below 👇
 1. Request to join the https://huggingface.co/hf-vision organization. 
 2. Upload an image to https://huggingface.co/datasets/hf-vision/course-assets, e.g. via the web UI.
-3. Get the URL (e.g. https://huggingface.co/datasets/huggingface-course/audio-course-images/blob/main/all_models.png) right click to "Download" button and copy the link.
+3. Get the URL (e.g. if there's blob in the link replace with resolve https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/all_models.png) right click to "Download" button and copy the link.
 4. Use that in standard markdown like ![image](link-to-image)
 
 ### Notebooks

chapters/en/Unit 0 - Welcome/welcome.mdx

@@ -2,18 +2,18 @@
 
 Dear learner,
 
-Welcome to the community-driven course on computer vision. Computer vision is revolutionizing our world in many ways, from unlocking phones with facial recognition to analyzing medical images for disease detection, enhancing public safety through surveillance systems, monitoring wildlife, and creating new images. Together, we'll dive into the fascinating world of computer vision!
+Welcome to the **community-driven course on computer vision**. Computer vision is revolutionizing our world in many ways, from unlocking phones with facial recognition to analyzing medical images for disease detection, monitoring wildlife, and creating new images. Together, we'll dive into the fascinating world of computer vision!
 
 Throughout this course, we'll cover everything from the basics to the latest advancements in computer vision. It's structured to include various foundational topics, making it friendly and accessible for everyone. We're delighted to have you join us for this exciting journey!
 
-In this page, you can find how to join the learners community, making a submission and getting a certificate, and more details about the course!
+On this page, you can find how to join the learners community, make a submission and get a certificate, and more details about the course!
 
 ## Assignment 📄
 
 To obtain your certification for completing the course, complete the following assignments:
 
 1. Training/fine-tuning a Model
-2. Building an application and hosting it on 🤗 Spaces
+2. Building an application and hosting it on Hugging Face Spaces
 
 ### Training/fine-tuning a Model
 
@@ -98,7 +98,8 @@ The course is organized into multiple units, covering the fundamentals and delvi
 
 ## Meet our team
 
-This is made by the Hugging Face Community with love! Our goal was to create a computer vision course that is beginner-friendly and that could act as a resource for others. Around 60+ people from all over the world joined forces to make this project happen. Here we give them credit:
+This course is made by the Hugging Face Community with love 💜! Join us by adding your contribution [on GitHub](https://github.com/johko/computer-vision-course).
+Our goal was to create a computer vision course that is beginner-friendly and that could act as a resource for others. Around 60+ people from all over the world joined forces to make this project happen. Here we give them credit:
 
 **Unit 1 - Fundamentals of Computer Vision**
 
@@ -138,9 +139,10 @@ This is made by the Hugging Face Community with love! Our goal was to create a c
 **Unit 8 - 3D Vision, Scene Rendering, and Reconstruction**
 
 - Reviewers: [Ratan Prasad](https://github.com/ratan), [William Bonvini](https://github.com/WilliamBonvini), [Mohammed Hamdy](https://github.com/mmhamdy), [Adhi Setiawan](https://github.com/adhiiisetiawan), [Ameed Taylor](https://github.com/atayloraerospace0)
-- Writers: [John Fozard](https://github.com/jfozard), [Vasu Gupta](https://github.com/vasugupta9)
+- Writers: [John Fozard](https://github.com/jfozard), [Vasu Gupta](https://github.com/vasugupta9), [Psetinek](https://github.com/psetinek)
 
-  **Unit 9 - Model Optimization**
+
+**Unit 9 - Model Optimization**
 
 - Reviewers: [Ratan Prasad](https://github.com/ratan), [Mohammed Hamdy](https://github.com/mmhamdy), [Adhi Setiawan](https://github.com/adhiiisetiawan), [Ameed Taylor](https://github.com/atayloraerospace)
 - Writer: [Adhi Setiawan](https://github.com/adhiiisetiawan)

chapters/en/Unit 1 - Fundamentals/Chapter 1 - Fundamentals of Computer Vision/motivation.mdx

@@ -36,7 +36,7 @@ Let's take the example of Indiana Jones running from a boulder. There is a ball
 ## The Motivation Behind Creating Artificial Systems Capable of Simulating Human Vision and Cognition
 
 Albeit they have similar input and output, human vision and computer vision are different processes. Sometimes they overlap. However, computer vision is primarily concerned with developing and understanding algorithms and models in vision systems and their decisions. It is not constrained to the creation of systems that replicate human vision. It can be used for problems that would be too tedious, time-consuming, expensive, or error-prone for humans to do.
-Our ball example is still a simple one, and you might not think that is super useful. However, a model capable of tracking a ball can be used in sports events to provide faster and more fair decisions during gameplay. With the popularization of image-to-text and text-to-speech models, we could also make live sports events more accessible for people who have vision disabilities by automatically tracking the ball and its players and describing it in real time. Thus, even simple use cases can have a positive impact on society. We will discuss more about this in Section 3 and chapter 14.
+Our ball example is still a simple one, and you might not think that is super useful. However, a model capable of tracking a ball can be used in sports events to provide faster and more fair decisions during gameplay. With the popularization of image-to-text and text-to-speech models, we could also make live sports events more accessible for people who have vision disabilities by automatically tracking the ball and its players and describing it in real time. Thus, even simple use cases can have a positive impact on society. We will discuss more about this in Section 3.
 
 We are now on the cusp of an AI renaissance.  A moment in time when we can train, deploy, and share our model freely. A moment when our models can detect things in images that we would not be able to see ourselves. 
 

chapters/en/Unit 1 - Fundamentals/feature-extraction/feature-matching.mdx

@@ -326,8 +326,7 @@ The best matches are visualized in green, while less certain matches are in blue
 ## Resources and Further Reading
 
 - [FLANN Github](https://github.com/flann-lib/flann)
-- [Image Matching Using SIFT, SURF, BRIEF and
-  ORB: Performance Comparison for Distorted Images](https://arxiv.org/pdf/1710.02726.pdf)
+- [Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images](https://arxiv.org/pdf/1710.02726.pdf)
 - [ORB (Oriented FAST and Rotated BRIEF) tutorial](https://docs.opencv.org/4.x/d1/d89/tutorial_py_orb.html)
 - [Kornia tutorial on Image Matching](https://kornia.github.io/tutorials/nbs/image_matching.html)
 - [LoFTR Github](https://github.com/zju3dv/LoFTR)

chapters/en/Unit 1 - Fundamentals/feature-extraction/feature_description.mdx

@@ -1,5 +1,7 @@
 # Feature Description
 
+Features are attributes of the instances learnt by the model to be later used to recognize new instances.
+
 ## How Can We Represent Features In Data Structures?
 
 Representing features in data is crucial for organizing and manipulating data effectively. Features, or attributes or variables, can be diverse, ranging from numerical values and categories to more complex structures like images or text. Some ways to represent features for computer vision tasks are:
@@ -55,7 +57,7 @@ You can learn more about SIFT using the following references:
 
 - [Introduction to SIFT (Scale-Invariant Feature Transform)](https://docs.opencv.org/4.x/da/df5/tutorial_py_sift_intro.html)
 
-[What is SIFT](https://www.educative.io/answers/what-is-sift)
+- [What is SIFT](https://www.educative.io/answers/what-is-sift)
 
 - [SIFT](https://www.cse.iitb.ac.in/~ajitvr/CS763/SIFT.pdf)
 
@@ -81,8 +83,6 @@ The key strengths of SURF lie in its computational efficiency, which is achieved
 
 You can learn more about SURF using the following references:
 
-- [Medium Blogpost - Introduction to SURF ](https://medium.com/@deepanshut041/introduction-to-surf-speeded-up-robust-features-c7396d6e7c4e)
-
-- [OpenCV Tutorial - Introduction to SURF (Speeded-Up Robust Features)] (https://docs.opencv.org/3.4/df/dd2/tutorial_py_surf_intro.html)
+- [OpenCV Tutorial - Introduction to SURF (Speeded-Up Robust Features)](https://docs.opencv.org/3.4/df/dd2/tutorial_py_surf_intro.html)
 
 - [Journal Paper - Feature Extraction Using SURF Algorithm for Object Recognition](https://www.ijtra.com/view/feature-extraction-using-surf-algorithm-for-object-recognition.pdf)