What do you want to understand deeply?

Multi-head self-attention, positional encodings, backpropagation, and LLM scaling laws.

Eigenvectors, Singular Value Decomposition (SVD), positive-definite matrices, and subspaces.

Raft consensus, consistent hashing, vector clocks, replication, and fault-tolerant storage.

Continuous and Discrete Fourier Transforms, convolution theorems, and spectral filtering.

Dynamic programming, shortest-path algorithms, balanced trees, and amortized complexity.

Bayes' theorem, Markov chains, KL divergence, variational inference, and Monte Carlo methods.

Gradients, Hessians, Lagrange multipliers, convex optimization, and Adam/SGD dynamics.

Container orchestration, service meshes, autoscaling, zero-trust networking, and observability.

Protein structure prediction, sequence alignment, hidden Markov models, and systems biology.

Itô's lemma, Black-Scholes PDEs, portfolio optimization, and algorithmic market microstructure.

Bloch sphere geometry, quantum gates, entanglement, Shor's algorithm, and error correction.

Lexing, parsing, SSA intermediate representations, register allocation, and garbage collection.