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The goal E University

Systems knowledge for AI engineers

Processes, memory, parallelism, containers, cryptography and code review — the computer science underneath the model, which decides whether the system can be run and trusted.

Knowledge nodes
43
From zero
about 32 h
Labs
4
See what you already know — no account

The diagnostic removes what you already know, so your path is usually much shorter.

What you can do afterwards

Labs along the way

You write the code. Tests you cannot see decide whether it holds up.

Lab: dot product, norm and cosine similarityDin the browser · about 40 minLab: matrix multiplication and one layer of a neural networkDin the browser · about 45 minLab: lists, loops and dictionariesCin the browser · about 40 minLab: your first Python functionsCin the browser · about 30 min

The whole path

Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map

AExplorer2 knowledge nodes
  1. Sequences and precise instructions
  2. Integer arithmetic and order of operations
BInvestigator4 knowledge nodes
  1. Algorithmic thinking
  2. Binary numbers and bits
  3. Fractions, decimals, and percentages
  4. Negative numbers in everyday life and AI
CBuilder8 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Python — strings and text processing
  6. Python — files, CSV and JSON
  7. Variables and algebraic expressions
  8. Powers and roots
DAI developer16 knowledge nodes
  1. Discrete mathematics: graphs and relations
  2. Time complexity and big-O notation
  3. Data structures: lists, stacks, queues, hash tables
  4. Python — functions, scope and exceptions
  5. Reading and understanding other people's code
  6. Python — modules, packages and virtual environments
  7. Git — version control
  8. Recursion
  9. The terminal and the shell
  10. Testing with pytest
  11. Logarithms
  12. Vectors
  13. Matrices and matrix multiplication
  14. NumPy — arrays and vectorisation
  15. Pandas — tables in Python
  16. SQL — the basics
EUniversity11 knowledge nodes
  1. Graph algorithms: BFS, DFS, topological order
  2. Dynamic programming
  3. Cryptography — hashes, signatures, keys
  4. Docker — containers
  5. Processes, threads and resource limits
  6. Code review
  7. Numerical stability and floating point
  8. Parallelism and why GPUs
  9. Data pipelines and ETL
  10. Data storage and formats: Parquet, Arrow
  11. Data versioning
FAI engineering2 knowledge nodes
  1. Floating-point formats: fp32, fp16, bf16
  2. The memory hierarchy and bandwidth