/deep-learning

time series forecasting with deep learning

Primary LanguageTeX

Deep Learning

When I switched to data science, I built my digital garden, datumorphism. I deliberately designed this digital garden as my second brain. As a result, most of the articles are fragments of knowledge and require context to understand them.

Making bricks is easy but assembling them into a house is not. So I have decided to use this repository to practice my house-building techniques.

I do not have a finished blueprint yet. But I have a framework in my mind: I want to consolidate some of my thoughts and learnings in a good way. However, I do not want to compile a reference book, as datumorphism already serves this purpose. I should create stories.

How to Contribute

This repository contains mostly markdown files. To make sure we have the same conventions, we have added markdownlint tools to pre-commit. So please install pre-commit then run the following command the first time you cloned the repository.

pre-commit install

Preview Requires Python

Install the requirements using

poetry install

Preview the docs:

poetry run mkdocs serve -s

Developing Notebooks

We use jupytext to sync the .py files to .ipynb files. .ipynb files are ignore in git. Please pair the .py file with the .ipynb using jupytext in jupyterlab first.

Optional Requirements

The pdf generation is done by the mkdocs-with-pdf plugin.

To generate PDF locally, please install cairo, Pango and GDK-PixBuf .

Install pango on Mac

When installing pango on Mac using homebrew, the path for DYLD_LIBRARY_PATH are not automatically updated. So we need to add the correct path for pango, harfbuzz, and fontconfig. For example,

export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/Users/itsme/homebrew/Cellar/pango/1.48.8/lib:/Users/itsme/homebrew/Cellar/harfbuzz/2.8.2/lib:/Users/itsme/homebrew/Cellar/fontconfig/2.13.1/lib