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Eight guided reading paths through the library. Pick the one that matches your math background and where you want to land. If nothing fits, the diagnostic calibrates a starting point from a 12-question placement.

Start doing something in five minutes

Why does my model overfit?

Six short lessons on overfitting and data leakage, followed by two model audits. Move the controls, check a forecast, evaluate a new store, and get an explanation. Start anywhere or continue where you left off. No account needed.

Try a 5-minute lesson
  1. 1. Overfitting · 3 lessons

    Spot the gap, try regularization, and check your model fairly.

  2. 2. Data leakage · 3 lessons

    Fit preprocessing on the right rows, keep folds separate, and forecast from the past.

  3. 3. Model audits · 2 cases

    Check a sales forecast, then test a different promise: predictions for a new store.

Math to ML

From mathematical foundations through learning theory to why ML models work.

Assumes: Comfortable with calculus, linear algebra, and probability at the undergraduate level.

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Engineer to ML Theory

Connect the tools you already use to the theorems behind them: when they work, when they break, and what the proofs actually say.

Assumes: You can train models and ship code; you want the math behind the methods.

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Stats to Deep Learning

Bridge classical statistics to modern deep learning: from MLE and exponential families to transformers.

Assumes: Statistics background: MLE, hypothesis testing, regression, exponential families.

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Master Linear Algebra

Linear maps, matrix operations, norms, eigenvectors, SVD, PCA, Jacobians, and matrix calculus. The algebra spine behind ML theory and neural networks.

Assumes: First-year calculus and basic matrix arithmetic. No prior linear algebra course required.

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LLM From Scratch

A two-stage decoder-only path: next-token prediction and causal masking, then KV cache, FlashAttention, and modern inference.

Assumes: You can read PyTorch and have built a small neural network; you want to understand the GPT stack end to end.

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Basic Neural Network From Scratch

Build a tiny MLP before jumping to transformers: linear layers, activations, losses, gradient descent, backprop, and regularization.

Assumes: Some Python and high-school calculus. No prior deep learning experience required.

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ML Research Readiness

An advanced path through probability, learning theory, optimization, deep learning, and modern model behavior for stronger paper reading.

Assumes: Undergraduate probability, real analysis, and linear algebra; you want to read modern ML papers with fewer missing prerequisites.

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Deep Learning Systems From Scratch

A 12-week shape, memory, and roofline track from linear layers to attention, KV cache, and accelerator performance.

Assumes: Comfortable with PyTorch, gradients, and backprop; you want the systems layer below the framework.

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