Clean up broken duckquill submodule references

drafts/2020-01-01-coding.md

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+title:  Coding Examples
+date:   2022-03-01 14:39:27 +0100
+author: Aron Petau
+excerpt: A selection of coding projects from my Bachelor's in Cognitive Science
+
+header:
+  teaser: /assets/images/sample_lr.png
+  overlay_image       : assets/images/sample_lr.png
+  overlay_filter      : 0.2
+  credit              : Aron Petau
+
+gallery:
+  - url: /assets/images/sample_lr.png
+    image_path: /assets/images/sample_lr.png
+    title: "A low-resolution sample"
+  - url: /assets/images/sample_hr.png
+    image_path: /assets/images/sample_hr.png
+    alt: ""
+    title: "A high-resolution sample. This is also called 'ground truth' "
+  - url: /assets/images/sample_sr.png
+    image_path: /assets/images/sample_sr.png
+    alt: " "
+    title: "The artificially enlarged image patch resulting from the algorithm"
+  - url: /assets/images/sample_loss.png
+    image_path: /assets/images/sample_loss.png
+    alt: ""
+    title: "A graph showing an exemplary loss function applied during training"
+  - url: /assets/images/sample_cos_sim.png
+    image_path: /assets/images/sample_cos_sim.png
+    alt: ""
+    title: "One qualitative measurement we used was pixel-wise cosine similarity. It is used to measure how similar the output and the ground truth images are"
+
+tags:
+  - ethics
+  - computer vision
+  - neural nets
+  - face detection
+  - object recognition
+  - GOFAI
+  - super resolution
+  - jupyter notebook
+  - google colab
+  - python
+  - tensorflow
+  - keras
+  - machine learning
+  - AI
+  - MTCNN
+  - CNN
+  - university of osnabrück
+created: 2023-07-26T23:59:59+02:00
+last_modified_at: 2023-10-01T20:15:26+02:00
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+
+## Neural Networks and Computer Vision
+
+## A selection of coding projects
+
+Although pure coding and debugging are often not a passion of mine, I recognize the importance of neural networks and other recent developments in Computer Vision. From several projects regarding AI and Machine Learning that I co-authored during my Bachelor Program, I picked this one since I think it is well documented and explains on a step-by-step basis what we do there.
+
+### Image Super-Resolution using Convolutional Neural Networks (Recreation of a 2016 Paper)
+
+Image Super-Resolution is a hugely important topic in Computer Vision. If it works sufficiently advanced, we could take all our screenshots and selfies and cat pictures from the 2006 facebook-era and even from before and scale them up to suit modern 4K needs.
+
+Just to give an example of what is possible in 2020, just 4 years after the paper here, have a look at this video from 1902:
+{% include video id="EQs5VxNPhzk" provider="youtube" %}
+
+The 2016 paper we had a look at is much more modest: it tries to upscale only a single Image, but historically, it was one of the first to achieve computing times sufficiently small to make such realtime-video-upscaling as visible in the Video (from 2020) or of the likes that Nvidia uses nowadays to upscale Videogames.
+
+{% include gallery caption="Example of a Super-Resolution Image. The Neural network is artificially adding Pixels so that we can finally put our measly selfie on a billboard poster and not be appalled by our deformed-and-pixelated-through-technology face." %}
+
+[The Python notebook for Image super-resolution in Colab]( https://colab.research.google.com/drive/1RlgIKJmX8Omz9CTktX7cdIV_BwarUFpv?usp=sharing){: .btn .btn--large}
+
+### MTCNN (Application and Comparison of a 2016 Paper)
+
+Here, you can also have a look at another, much smaller project, where we rebuilt a rather classical Machine learning approach for face detection. Here, we use preexisting libraries to demonstrate the difference in efficacy of approaches, showing that Multi-task Cascaded Convolutional Networks (MTCNN) was one of the best-performing approaches in 2016. Since I invested much more love and work into the above project, I would prefer for you to check that one out, in case two projects are too much.
+
+ [Face detection using a classical AI Approach (Recreation of a 2016 Paper)](https://colab.research.google.com/drive/1uNGsVZ0Q42JRNa3BuI4W-JNJHaXD26bu?usp=sharing){: .btn .btn--large}