Welcome to my personal digital knowledge garden, a collection of notes and resources that I started to compile a couple of years ago as my best attempt to become a somewhat functional information junkie. Here I curate, organize and catalog the stuff I read
skim over everyday.
# About digital knowledge gardens
The concept of a digital knowledge garden, a.k.a. “second brain”, has been around for quite some time and is related to that of personal knowledge management. Digital gardens build upon note-taking methodologies such as Zettelkasten or Evergreen. In short, a digital garden is something in between a blog and a wiki; a way to accumulate personal knowledge over time in an explorable space and in a non-linear fashion, while benefiting from fancy features such as (bidirectional) links between different topics, and visual graphs or mind maps. This GitHub repository offers a complete list of tools and workflows for aspiring gardeners wishing to grow a knowledge garden.
Throughout my journey with personal knowledge management, I have used a combination of different tools and practices with varying levels of success: curating lists of links as bookmarks or Pocket collections, compiling notes with Evernote or OneNote, organizing ideas in mind maps with XMind, and collecting bibliographic data in Zotero. Since 2020, I started to use Obsidian for growing my digital knowledge garden and managing my markdown notes locally, together with the Copilot plugin running a Llama 3.2 LLM for querying from the vault. The vault gets published on GitHub pages thanks to Quartz. I plan to find the time to write how I use LLMs to interact with my vault ;)
# Main motivation for creating this knowledge garden
Keeping up with the literature related to Artificial Intelligence (AI) and Machine Learning (ML) is impossible very difficult and, although tools like the
Deep Learning Monitor might be of help, the “Fear Of Missing Out”
(FOMO) information is hardly avoidable, especially if you are an information junkie like me. This knowledge garden is not and will never be an exhaustive mapping of all there is to know about AI or ML. It is also not aimed at teaching anyone or to be pedagogical, though it can certainly point you to a multitude of educational resources. The content of this knowledge garden is based merely on my personal research notes, the topics that I have been interested in or that I have come across in my work as a researcher in AI/ML and as a Data Scientist.
# What to find in here
Most notes in this knowledge garden are focused on specific topics (e.g., LLMs) but others are broader maps of content (e.g., Deep Learning). The notes are composed of common subsections:
- Resources: definitions, wikipedia entries, blog posts and other useful stuff.
- Books: well… books, mostly free or open source.
- Courses: online and free/open courses mostly by recognized universities and institutions.
- References: peer-reviewed publications and papers.
- Code: open source code and relevant libraries.
- Talks: talks, video summaries and video podcasts.
Most of the entries (bullet points) in a note carry a specific tag, depending on the subsections they belong to, for example: #PAPER, #COURSE, #BOOK or #CODE. The following are some maps of content or important pages you may want to start from:
Feel free to look around, either by exploring the maps of content above, checking out the main index of notes related to AI, using the search box, or by interacting with the mind map at the bottom of the page. Expect some broken links and all sort of bugs and errors. I hope you find something useful in this knowledge garden. Enjoy!