Playgrounds
Machine learning you can operate. Every lab makes one normally-invisible idea visible, lets you change it, and shows the consequence immediately — including what happens when you push it until it breaks.
18 of 18 labs are ready to run. The rest are planned.
Foundations
3Gradient Descent
How a model actually learns: it measures how wrong it is, works out which way to move each parameter to be less wrong, and takes a small step that way.
7 stages
Bias-Variance Tradeoff
How model complexity drives a tradeoff between capturing the underlying signal (bias) and overreacting to noise (variance).
5 stages
Train/Test Split & CV
How data partitioning works: splitting testing data out prevents overfitting, while cross validation allows for model evaluation across the entire training set robustly.
4 stages
Supervised Learning
5k-Nearest Neighbors
This model has no equation and learns nothing at training time. Its decision boundary is an emergent shape, produced entirely by asking which labelled points are nearest.
3 stages
Decision Trees
how a model uses simple axis-aligned splits to isolate classes by reducing impurity.
4 stages
Logistic Regression & Decision Boundaries
A decision boundary is not an intrinsic property of the model; it is just a chosen threshold on top of a continuous probability surface.
3 stages
Supervised Ensembles: Random Forests & Boosting
How multiple simple decision trees can collaborate to model complex boundaries without overfitting, and how boosting forces focus on difficult examples.
5 stages
Support Vector Machines
You are seeing the margin boundaries and the hidden higher-dimensional space where a simple flat plane can separate complex patterns.
4 stages
Unsupervised Learning
2K-Means Clustering
Clustering without labels is a loop of two operations that each improve one thing while holding the other fixed — and it always converges, whether or not it converged to anything sensible.
3 stages
Principal Component Analysis (PCA)
how PCA finds the direction of maximum variance to reduce dimensionality while preserving information
4 stages
Neural Networks
1Modern ML
2Attention
Attention is not a metaphor. It is a matrix of real numbers saying how much each token reads from each other token, and every row of it sums to exactly one.
4 stages
Transformer Decoding
Generation strategies manipulate an underlying probability distribution before the next token is sampled.
4 stages
Generative AI
3Embeddings & Vector Space
Words as points in high-dimensional space where distance means semantic similarity and directions carry conceptual meaning.
4 stages
Diffusion Models
Generation as an iterative denoising process rather than a one-shot creation.
4 stages
LoRA (Low-Rank Adaptation)
How decomposing a massive weight update into two low-rank matrices drastically reduces trainable parameters while maintaining learning capability.
4 stages
Evaluation & RL
2Q-Learning
How an agent learns the value of state-action pairs (Q-values) through trial, error, and delayed rewards in an environment.
3 stages
Threshold, Confusion Matrix & ROC
A classifier does not output a class. It outputs a score, and someone chooses where to cut — so accuracy, precision and recall are properties of that choice rather than of the model.
3 stages