Loading your workspace…
Loading your workspace…
Concepts
Explore the intuition, inspect a working visual, and connect it to the math and code. Every article is free to read, with a guided session available when you want to ask follow-up questions.
Rotate a loss surface and compare step sizes.
Compare directions in a three-dimensional vector space.
Compare demonstrations, policy updates, and preferences.
Attention Mechanisms
Turn compatibility scores into attention weights, then combine values. Explore the geometry, normalization, and limits of the mechanism.
9 min read Visual inside
Batch Normalization
Normalize activations across a batch, apply a learned scale and shift, and understand why training and evaluation behave differently.
8 min read Visual inside
Transformer Architecture
Follow attention, feed-forward layers, normalization, and residual connections through a Transformer block.
9 min read Visual inside
Backpropagation
Trace local derivatives backward through a computation and connect the chain rule to a manual implementation.
9 min read Visual inside
Positional Encoding
Compare positional signals at different frequencies, then connect absolute encodings and RoPE to attention.
7 min read Visual inside
Bias-Variance Tradeoff
Separate systematic error from sensitivity to the training sample. Read an error decomposition without assuming every model follows a U-shaped curve.
8 min read Visual inside
Logistic Regression
Connect a linear score to a sigmoid probability, a cross-entropy loss, and a decision threshold.
8 min read Visual inside
Principal Component Analysis (PCA)
Compare centered data with its projection onto a principal direction. Understand what variance retention does and does not preserve.
8 min read Visual inside
Gradient Boosting & XGBoost
See one residual-fitting update, then connect the additive model to gradient-based boosting for other losses.
8 min read Visual inside
LoRA and PEFT
Follow a frozen weight path and a trainable low-rank correction. Understand parameter savings, scaling, and merging.
8 min read Visual inside
RLHF (Reinforcement Learning from Human Feedback)
Distinguish demonstrations, preference comparisons, reward models, and policy updates. Compare SFT, PPO, and DPO visuals.
9 min read Visual inside
KV Cache
See which token projections caching saves, what attention must still read, and how memory use grows with context.
8 min read Visual inside
Tokenization & BPE
Follow a pair of vocabulary merges and distinguish learning merge rules from applying a tokenizer to new text.
7 min read Visual inside
Embeddings
Compare vectors by direction and magnitude, and connect embedding geometry to retrieval and representation learning.
7 min read Visual inside
Retrieval-Augmented Generation (RAG)
Follow retrieval into a grounded prompt, then examine chunking, ranking, evidence quality, and generation failures.
8 min read Visual inside
Temperature, Top-k & Top-p Sampling
Watch temperature reshape probabilities and distinguish that transformation from top-k, top-p, and sampling.
7 min read Visual inside