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Thanks for the comment. I will be chronicling my progress on the blog!


BTW I submitted your post to /r/LocalLLaMA. I expect it will see some good attention there. It seems to be the best community around (outside of various Discords I don't participate in) for people doing ML at home, even endorsed by Andrej Karpathy himself. https://old.reddit.com/r/LocalLLaMA/comments/1970zhf/merging...


Thanks! Did not know about /r/LocalLLaMA


Thanks for reading and yes you are right, the input audios are clips of music;

MusicCaps [1] is a dataset containing pairs of music audio and natural language description of the clip; the reason why the result is good imo is because the trained model was able to generate a description with features of the ground truth

[1] https://huggingface.co/datasets/google/MusicCaps


Haha thanks; the html is on Github https://github.com/moomou/moomou.github.io/blob/master/resum... and from there you can see the imported css etc.; be warned though, the resume & css have been accumulated over the years so they are not particularly clean


Thanks! I was hoping I'd discover some nice LaTex class, but alas :)


Noted. Will do. Definitely plan to make to release on all OS - but focusing on gathering feedback right now.


Right now, Sloth runs on OSX 10.8 or later. There is no storage limit; items not used will be autodeleted after n days (set in config).

Love to hear what you think of it!


https://ohsloth.com

Sloth is a modern day copy and paste manager that allows you to search snippets by both content and context. Context here being what program you copied from and the type of text (whehter it's an ip, python, or bash script, etc.).

Right now I am working on a custom machine learning model to do better text classification.

It's built on electron and I am adding slack integration to create a shared clipboard. Essentially, slack would act as a cloud storage.


https://ohsloth.com/

Ohsloth is an open source copy and paste manager that aims to provide contextual actions based on snippet content.

It is still super early but it definitely could use some design love!


For anyone interested, I have been really interested in deep learning and have been using the following resources:

For image processing (CNN) https://cs231n.github.io/

For natural language processing (RNN) http://cs224d.stanford.edu/syllabus.html

I also found the following coursera helpful https://www.coursera.org/course/neuralnets https://www.coursera.org/learn/machine-learning


These are great resources. There is also a ML class offered by Georgia Tech at Udacity under the OMSCS program. https://www.udacity.com/wiki/ml/GT


Thank you, a lot of the papers in DL/Sparse Coding aren't as clear as the sources you shared.


Does anyone know how to access past courses on coursera if you didn't sign up in a timely manner?


I think you just sign in and click on a big green button "Go to Course" on the right - at least it worked for me with neuralnets course by Hinton, which is really great.


It depends on the course, some courses are kept available and others are shut down after the action is over. I think the choice is left up to the instructors. I started downloading all of the videos when I sign up for a course for this reason actually, because I tend to go through them too slowly to finish with the main group.


Thanks. I really appreciate it when folks like you on HN post learning materials in the comments.


Thanks, i will see it, seems very interesting.


Working on providing contextual information to whatever you do online: https://cueb.io/


I never knew that redis can be extended for geo. This is really cool.

I researched geodb a while back and I only found PostGIS but I recently found out that elastic search also supports geo features.

Shameless plug - This is the simple API I built on top of PostGIS https://github.com/moo-mou/GeoGo


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