my personal notebook for learning ai from scratch.
this repo is not meant to be a polished course or tutorial.
it's a collection of my notes, learnings, resources, plans, experiments, observations, and project ideas as i progress through the ai/ml space.
the goal is simple:
- understand concepts deeply
- build strong fundamentals
- keep track of what i'm learning
- create a reference i can revisit in the future
- document projects and experiments along the way
notes explaining fundamental concepts in simple language.
examples:
- tokens
- parameters
- context windows
- transformers
- embeddings
- attention
- fine-tuning
- quantization
- rag
- agents
a living document containing:
- what i'm currently learning
- what i've completed
- what i'm planning to learn next
this will evolve as my understanding grows.
a collection of useful learning material including:
- youtube playlists
- courses
- blogs
- research papers
- github repositories
- books
- documentation
for each resource i may also add:
- notes
- takeaways
- timestamps
- things worth revisiting later
notes taken while watching videos, reading blogs, exploring repositories, or following courses.
these notes are written for future me.
the goal is not perfect documentation.
the goal is quick understanding when revisiting a topic months later.
small hands-on experiments to better understand concepts.
examples:
- tokenization experiments
- embedding experiments
- transformer implementations
- training toy models
- inference experiments
- rag prototypes
- agent workflows
a collection of ideas that i may explore in the future.
each idea may include:
- problem statement
- rough architecture
- learning objectives
- implementation notes
a simple log of:
- what i'm learning
- completed topics
- active topics
- upcoming topics
this helps me stay consistent and measure progress over time.
.
├── README.md
├── roadmap
│ └── learning-plan.md
├── notes
│ ├── terminology.md
│ ├── transformers.md
│ ├── embeddings.md
│ ├── rag.md
│ └── agents.md
├── resources
│ └── resources.md
├── experiments
├── projects
└── progress
└── learning-log.md
- building strong fundamentals
- understanding how llms work internally
- learning transformers from first principles
- understanding training vs inference
- learning how modern ai applications are built
learn slowly.
understand concepts instead of memorizing them.
focus on fundamentals first.
build things whenever possible.
document everything worth revisiting.
future me should be able to open this repo and quickly understand what i learned, why i learned it, and where i left off.