Roadmap
The AI learning roadmap
Everything an AI researcher stands on — and how each topic builds on the last. Start at the foundations, climb to the top.
Tap a topic to see what it covers; hover to trace its connections.
Goal
AI Researcher
Reads, reproduces, and pushes the state of the art — combining every layer below.
Builds on: Fine-tuning & RLHF, RAG & Retrieval, Agentic Workflows
Building with LLMs
Fine-tuning & RLHF
Adapting pretrained models — SFT, LoRA, preference tuning.
Builds on: Transformers & LLMs
RAG & Retrieval
Grounding models in external knowledge with retrieval.
Builds on: Transformers & LLMs
Agentic Workflows
LLMs that plan, use tools, and act in multi-step loops.
Builds on: Transformers & LLMs
Language Models
Transformers & LLMs
Attention, scaling, pretraining — the modern backbone.
Builds on: Deep Learning
Read the article →Deep Learning
Deep Learning
Neural networks, backpropagation, training at scale.
Builds on: Optimization, Core ML
Core ML
Optimization
Gradient descent and how models actually learn.
Builds on: Linear Algebra, Calculus
Read the article →Core ML
Regression, classification, generalization, evaluation.
Builds on: Probability, Statistics, Programming, Optimization
Math foundations
Calculus
Derivatives and gradients that drive learning.
Probability
Uncertainty, distributions, likelihood.
Statistics
Estimation, inference, hypothesis testing.
Programming
Python, NumPy, PyTorch — making it run.