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Reviewed general punctuation and fixed some broken links #251

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merged 58 commits into from
Jul 17, 2024

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sergiopaniego
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In this PR I've:

  • Reviewed the general punctuation of the units and updated it.
  • Fixed some links that appeared broken.

I've used https://moon-ci-docs.huggingface.co/docs/computer-vision-course/pr_246/en/Unit%200%20-%20Welcome/welcome as a source to find the issues.

Let me know if the course is updated elsewhere and I'll update it accordingly.
This PR may cause merge issues with #250, so I could update it once the former is merged

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We do not have dots on headings, resources, clauses (that aren't sentences), end of parenthesis that already have dots
Also if you have time and if you feel like it, you can fix broke image links (I think there's a few in chapter 9)

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Thanks for the feedback @merveenoyan! Would you like me to make modifications regarding the punctuation?
I'll review Chapter 9 for broken image links.
For now, I've merged the updated main branch 😄

@@ -16,12 +16,12 @@ As we already know, optimizing the model is important in before the deployment s
There are several techniques in the model optimization, which will be explained in the next section. However, this section will briefly describe several types:
1. Pruning: Pruning is the process of eliminating redundant or unimportant connections in the model. This aims to reduce model size and complexity.

![Pruning](https://huggingface.co/datasets/hf-vision/course-assets/raw/main/pruning.png)
![Pruning](https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/pruning.png)
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Can we consider the links in Unit 9 fixed in this way?

@@ -144,7 +144,7 @@ visualize_grid(grid, encoded_grid, resolution)

The output should look something like the image below:

![encoding](https://huggingface.co/datasets/hf-vision/course-assets/blob/main/nerf_encodings.png)
![encoding](https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/nerf_encodings.png)
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This Unit image link is also updated

@@ -70,7 +70,7 @@ The feature map will be the same size as the original data. The result of the co
If we keep applying the convolution, we get the following feature map.

<div class="flex justify-center">
<img src="https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/CNNs/2d-feature_map.png" alt="2D Feature Map">
<img src="https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/CNNs/2d_feature_map.png" alt="2D Feature Map">
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Image broken link updated here

@sergiopaniego
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I've indicated where the update links are located.
I could even generate a separate PR just with the broken links if that's easier for merging 😃

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Thanks a lot! helps greatly :)

- Statistical
- Geometrical
- Mathematical
- Logical.
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these aren't full sentences so there's no need

- [User Manual](https://docs.blender.org/manual/en/latest/0)
- [Awesome-blender -- Extensive list of resources](https://awesome-blender.netlify.app)
- [Blender Youtube Channel](https://www.youtube.com/@BlenderOfficial)
- [User Manual](https://docs.blender.org/manual/en/latest/0).
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these aren't sentences so no need

- [Developing digital twins of multi-camera metrology systems in Blender](https://iopscience.iop.org/article/10.1088/1361-6501/acc59e/pdf_)
- [Generate Depth and Normal Maps with Blender](https://www.saifkhichi.com/blog/blender-depth-map-surface-normals)
- [Object detection with synthetic training data](https://medium.com/rowden/object-detection-with-synthetic-training-data-f6735a5a34bc)
- [Developing digital twins of multi-camera metrology systems in Blender](https://iopscience.iop.org/article/10.1088/1361-6501/acc59e/pdf_).
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these aren't sentences so no need

@@ -45,21 +45,21 @@ Now, first we need to understand the formats in which these point clouds are sto
**Why?**

- `point-cloud-utils` supports reading common mesh formats (PLY, STL, OFF, OBJ, 3DS, VRML 2.0, X3D, COLLADA).
- If it can be imported into [MeshLab](https://github.com/cnr-isti-vclab/meshlab), we can read it! (from their readme)
- If it can be imported into [MeshLab](https://github.com/cnr-isti-vclab/meshlab), we can read it! (from their readme).
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there's already exclamation mark here

- Batch Normalization layer
- ReLU activation
- Conv2D layer with Tanh activation
- Conv2D layer.
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these aren't sentences so no need

chapters/en/unit5/generative-models/gans-vaes/stylegan.mdx Outdated Show resolved Hide resolved
2. AdaIN (Adaptive Instance Normalisation)
3. Concatenation of Noise vector
Diagram (b) is the proposed StyleGAN architecture. It has the following main components:
1. A mapping network.
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these aren't sentences so no need

![Rectified Left Image](https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/3d_stereo_vision_images/rectified_left_frame.jpg?download=true)

Rectified Right Image
Rectified Right Image.
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these aren't sentences so no need

- Write an application once, deploy it anywhere on your preferred device, language and OS.
- has minimal external dependencies
- Reduces first-inference latency by using the CPU for initial inference and then switching to another device once the model has been compiled and loaded to memory
- has minimal external dependencies.
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these are subsentences in form of lists so no need


## EdgeTPU

### Overview

Edge TPU is Google’s purpose-built ASIC designed to run AI at the edge. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge.
The benefits of using EdgeTPU includes:
- Complements Cloud TPU and Google Cloud services to provide an end-to-end, cloud-to-edge, hardware + software infrastructure for AI-based solutions deployment
- High performance in a small physical and power footprint
- Complements Cloud TPU and Google Cloud services to provide an end-to-end, cloud-to-edge, hardware + software infrastructure for AI-based solutions deployment.
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these are subsentences

@merveenoyan
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@sergiopaniego given the amount of fixes I think you should add your name to credits

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I've updated the files with all the suggestions, accepted your commits, and restored a broken link to a chapter.
Please let me know if there's anything else that needs attention!

@sergiopaniego given the amount of fixes I think you should add your name to credits

How should I add myself @merveenoyan?

Thanks for the in-depth review!

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Thanks! be mindful that we are looking for atomic PRs otherwise it will take a long time to merge changes

chapters/en/unit0/welcome/welcome.mdx Outdated Show resolved Hide resolved
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@sergiopaniego when we make comments we would like to make them general, please reflect them across the text or I have to fix them one by one which takes a lot of time

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@sergiopaniego you can give yourself credit on credits section in 0th chapter

sergiopaniego and others added 15 commits May 15, 2024 10:04
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@sergiopaniego when we make comments we would like to make them general, please reflect them across the text or I have to fix them one by one which takes a lot of time

Thank you for all the suggestions and your thorough review. Apologies for my delayed responses (been busy with work and a conference).

I've accepted all your suggestions. I'll conduct a detailed review of the entire PR to ensure that all the suggestions are reflected.

@sergiopaniego
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Hi @merveenoyan 😄 I've conducted the review and the PR is now ready to be checked.

I've removed some punctuation thanks to your suggestions and updated some broken links that I've found while reviewing (1c20a24)

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@johko johko left a comment

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a very late review, but LGTM 👍

will merge it (finally)

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Thanks @johko! 😄

@johko johko merged commit f532359 into johko:main Jul 17, 2024
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3 participants