awesome-openbsd
start-machine-learning-in-2020
awesome-openbsd | start-machine-learning-in-2020 | |
---|---|---|
4 | 23 | |
423 | 1,426 | |
- | - | |
3.8 | 6.3 | |
9 days ago | over 2 years ago | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-openbsd
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Privacy Guides for OpenBSD
Great list, maybe double check with https://github.com/ligurio/awesome-openbsd
- Red and Black
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Role of this sub; DaemonForums; Clogging the airwaves -- thoughts?
4 - after awhile i imagine that you (like me) will become familiar with some of the other sources of information - in particular, i have often run through the "a curated list..."-githubs - but when i started to pull up that link in this browser - i got two ( lol )... thus, even those lists become stale or abandoned... apparently they both refer to the same twitter-account: https://twitter.com/openbsdnow ... bottom line: read the docs (on your own system, if it is older - or online for the latest/greatest)...
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Trying to find a place where to chat
There are some OpenBSD chat communities in other platforms: https://github.com/ligurio/awesome-openbsd#chats
start-machine-learning-in-2020
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The most useful (and free) resources for learning AI + my tips & recommendations
A Complete Roadmap for Beginners in Machine Learning in 2021+ many valuable resources for any data scientist / AI workers or enthusiasts + how to stay up-to-date with news The complete article with all my tips: https://www.louisbouchard.ai/learnai/
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I recently started fiddling around with GPT 3, and I’m looking for books that will help me on my journey. Any recommendations?
Here's the guide for machine learning with many interesting books: https://www.louisbouchard.ai/learnai
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A Complete Roadmap for Beginners in Machine Learning with many valuable resources for any AI workers or enthusiasts + how to stay up-to-date with news
All the links on GitHub: https://github.com/louisfb01/start-machine-learning-in-2020
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How to start learning AI?
You can follow this guide to learn ai from nothing for free : https://www.louisbouchard.ai/learnai
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Barbershop: Try Different Hairstyles and Hair Colors from Pictures (GANs+)
Well I already have a guide I made for learning machine learning, but not GANs in particular. For machine learning in general and improve your skills you can check this out: https://www.louisbouchard.ai/learnai For GANs, i just read papers about it and implemented some, starting with pix2pix which was quite clear
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Looking for Resources
The guide: https://www.louisbouchard.me/learnai
- A friendly approach to maths and coding in ML (2021-updated)
What are some alternatives?
selfhosted-music-overview - A table listing software network services which can be hosted on your own servers
PES-2021-Cheat-Table - Cheat Table for eFootball PES 2021
frontend-challenges - A public list of open-source challenges from companies around the world
projectlearn-project-based-learning - A curated list of project tutorials for project-based learning.
herbe - Daemon-less notifications without D-Bus. Minimal and lightweight.
nexify.io - Develop your skills with programming courses, explained step by step, to learn by building things.
allainews_sources - A list of online news & info sources in the AI/ML/Data Science space
learn-monogame.github.io - Documentation to learn MonoGame from the ground up.
Awesome-Black-Friday-Cyber-Monday-deals - 🟢 2023 Deals Live - Black Friday & Cyber Monday, Christmas & Holidays Deals for Developers, Techies, & Entrepreneurs,
human-memory - Course materials for Dartmouth course: Human Memory (PSYC 51.09)
computer-sciences - Code bites from Computer Sciences @ Politecnico di Torino
internet-explorer - Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset.