Probably a useful link to add to this repo (under Mathematica / Wolfram Language):

https://resources.wolframcloud.com/NeuralNetRepository/

The Wolfram Neural Net Repository is a public resource that hosts an expanding collection of trained and untrained neural network models, suitable for immediate evaluation, training, visualization, transfer learning and more.

Articles are self explanatory from their headings. I feel adding tutorial specifically under R subsection is somewhat should not be there.

Just like other good articles by great authors, their heading should be kept unchanged.

Reached this awesome list from the : akullpp/awesome-java .

https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/details/java-jvm.md#java

Using Java for Artificial Intelligence (Tweet)

This link is broken.

Thank you!

Purpose : To add materials of Computer Vision field into the repository as it had none.

Contents : Motivation (intro to computer vision), Digital image processing, Opencv and its tutorials, Courses from other organisations, Conferences to follow and some famous computer vision blogs.

@neomatrix369 please check the pull request. Waiting for your feedback on it.

Computer vision topic should focus on the basics of image processing , different filters used, and will then move onto CNNs and deep learning methods of computer vision.

https://github.com/virgili0/Virgilio/blob/master/serving/inferno/computer-vision/object-tracking/object-tracking.ipynb is giving error no. 404, under heading Image Processing under Computer Vision in /details/julia-python-and-r.md

Signed-off-by: Samhita Alla [email protected]

Flyte is a workflow automation platform for complex, mission-critical data and ML processes at scale. A detailed overview of the features can be seen in the GitHub repo.

This PR is an extension to introduction to code-mixing and code-switching!

• added a few important metrics and their descriptions useful for modeling code-mixing corpus
• included useful resources which described the metrics in detail

Requesting a new issue to be assigned to me:

Add automated testing to ensure all links in markdown files are not broken, and alert when there are issues with links.

@neomatrix369 - let me know if you want me to work on this issue or if it's not helpful for your project! I would love to contribute as part of hacktoberfest.

As see from #53 we can have broken/dead links, links that once worked can be unavailable for reasons outside the control of this project/repo!

Hence I have decided to manually scan (for now) the repo from time to time for such links and fix them - if there is one. Here are the steps to take:

New broken/dead links

• find missing links using, markdown-link-check (see https://www.npmjs.com/package/markdown-link-check to find out how to install and use it)
• once installed, use the below command in the root of the project:

$ ls **/*.md | xargs -n 1 markdown-link-check --quiet

### This recursively finds all markdown files in the repo, 
### scans them and only reports those files which have 
### broken/dead links in them. 

• try to fix the broken/dead links by hand
• we are only looking for HTTP response code of 404, any other response codes can be ignored
• if a fix cannot be found, best mark the link with a '[deadlink]' marker
• in certain cases it's a good idea to leave the old link with the '[deadlink]' marker next to it even though we have found a new working one

Existing broken/dead links across the repo

Existing dead/broken links are marked with the '[deadlink]' marker.

As part of this issue, fixing these links is also helpful - although if they are left in there it's cause their fix wasn't immediately available or found on searching on the relevant sources.

Eventually, we can automate the task of finding such links via a GitHub action during GitHub events like commit, push or pull request creation.

On the back of this discussion, @shahanesanket and I will take this further https://github.com/pandas-profiling/pandas-profiling/issues/278, some high-level ideas:

• Missing value analysis
• Text length analysis
  • 2.1 min, max, average, quantiles
  • 2.2 freq words, infrequent words (can include the deepmoji project's tokenizer. it's very robust)
  • 2.2 word cloud. (if it isn't a far stretched goal)

@shahanesanket let's continue with our discussions here.