notes
bench-warmers
| notes | bench-warmers | |
|---|---|---|
| 3 | 6 | |
| 1 | 54 | |
| - | - | |
| 6.4 | 9.7 | |
| 5 months ago | 6 days ago | |
| Python | ||
| - | 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.
notes
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Using Github to write my notes has helped me retain knowledge immensely.
I didn't really use a guide to learn how to use them all together, I simply wrote my notes on VSCode (now Obsidian) and saved them as markdown files. I then used Github Desktop to add it as a local repository and then to publish it as a remote repository on Github (https://github.com/BCDShaw/notes/blob/main/readme.md).
bench-warmers
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What to do next?
i have more ideas than I know what to do with, help yourself: https://github.com/dmarx/bench-warmers
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Any ideas for NLP end-to-end projects or blogs for a beginner with a linguistics background to boost their CV?
you're welcome to help yourself to my ideas (no guarantees that they're any good or even comprehensible, I do a lot of my brainstorming while high). here's my brainstorming space, scroll down for a categorized ToC: https://github.com/dmarx/bench-warmers
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[R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
I've decided to just lean into it and am literally just giving my ideas away. https://github.com/dmarx/bench-warmers
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Using Github to write my notes has helped me retain knowledge immensely.
it might sound like a lot, but it's actually really lightweight and easy to use. Check it out: https://github.com/dmarx/bench-warmers
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We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
you've sort of become victims of your own success: as another pandas dev mentioned, you want to preserve backwards compatibility and this significantly complicates any restructuring. I'm sympathetic and am not sure what the best solution here would be. I had this idea last night but i'm not sure I like this approach either.
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Need help on finding an area where machine learning is applicable on day-to-day life but not implemented already
To be clear, i'm talking about e.g. vision impaired, hearing impaired, etc. Here's an example of a project idea in this space (possibly a bit more ambitious than what you're looking for but if you think you could tackle this I encourage you to take a stab at it): https://github.com/dmarx/bench-warmers/blob/main/automated-video-description.md
What are some alternatives?
khoj - Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g mistral) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.
quickadd - Parse natural language time and date expressions in python
LLaMA-Adapter - [ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters
obsidian-omnisearch - A search engine that "just works" for Obsidian. Supports OCR and PDF indexing.
python-bigquery-pandas - Google BigQuery connector for pandas
obsidian-releases - Community plugins list, theme list, and releases of Obsidian.
pandas-stubs - Public type stubs for pandas
syncthing-android - Wrapper of syncthing for Android.
logseq - A local-first, non-linear, outliner notebook for organizing and sharing your personal knowledge base. Use it to organize your todo list, to write your journals, or to record your unique life.
scikit-learn - scikit-learn: machine learning in Python