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nbs/.gitignore

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+/.quarto/

nbs/notes/20240427-update-resume.qmd

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 ### Steps:
 
 - [Install and experiment with llama.cpp](/extras/20240427-llamacpp-experiment.html)
-- TBD
+- TBD 

nbs/notes/20240506-notes-to-self.qmd

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+# Notes to self
+
+List of notes to my future self
+
+
+
+
+
+
+## Tech:
+
+### Conda:
+
+- use `https://anaconda.org/<channel_name>/repo` to list all packages in the channel
+  from: <https://stackoverflow.com/a/70254570/674013>
+
+- a lot of ways to search packages:
+  <https://stackoverflow.com/questions/43222407/how-to-list-package-versions-available-with-conda>
+
+### Docker:
+
+- use [multi-stage builds](https://docs.docker.com/build/building/multi-stage/) to reduce image size and improve security
+
+
+
+## AI:
+

nbs/notes/20240510-links.qmd

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+# Links and Refs
+
+My unorganized list of links
+
+## AI
+
+### Learning AI
+
+#### books, tutorials
+- [Sebastian Raschka](https://sebastianraschka.com/books/) - good book to learn AI from scratch
+- [Dive into Deep Learning](https://d2l.ai) - learn ai from the concepts, the context, and the code. Academic.
+- [Fast AI](https://www.fast.ai/) - Practical deep learning for coders, with less math. Very practical
+- [Neural Network & Deep Learning](http://neuralnetworksanddeeplearning.com) - Free online book
+
+#### links
+- [AI Canon](https://a16z.com/ai-canon/) - collections of links to learn AI
+- [Ilya Sutskever to John Carmack list of links](https://arc.net/folder/6D437AC8-7D42-4B9D-AC58-34A7EF6010D4) - Ilya Sutskever “If you really learn all of these, you’ll know 90% of what matters today” (2024) - very technical
+
+#### refs, cheatsheets
+- [Google's Machine Learning Glossary](https://developers.google.com/machine-learning/glossary) - in case I forget something
+- [AI Wiki](https://machine-learning.paperspace.com/) - A repository of machine learning, data science, and artificial intelligence (AI) terms for individuals and businesses
+
+

nbs/tools/mlai-tools.qmd

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 # ML/AI Tools/Links
 
-Links and my comments on tools that I found interesting around the web
+Links and my comments on tools that I found interesting around the web - in no particular orders
 
 ## Refs:
 - [Mudiarto ML/AI stars](https://github.com/stars/mudiarto/lists/ml-ai) - My collection of ML/AI stars as I stumbled upon them
 
 ## Tools I use
 - TBD
 
+
+### Jupyter
+- I am deciding between [marimo](https://marimo.io/) and jupyter for doing my ML/AI learning. Marimo seems good,
+  but for now, it seems jupyter is more standard and more supported. So I will use Jupyter for now, and probably
+  experiment with marimo later
+
+- I am experimenting with [jupyter-ai](https://jupyter-ai.readthedocs.io/en/latest/index.html)
+  - installation was straightforward
+  - [jupyter-ai experiment notebook](https://github.com/mudiarto/ml-notebooks/blob/dev/notebooks/misc/test-jupyter-ai.ipynb)
+
+
+### MLFlow
+- I want to be able to keep track of my experiment instead of doing it randomly. 
+
 ## Tools that seems useful
-- TBD
+- TBD
+