Concept library
638 concepts across 13 domains and 52 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
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01Foundations
02Transformer Internals
03Training & Fine-Tuning
04Reinforcement Learning
05Inference, Systems & Hardware
06Applied LLM Engineering
07Reasoning, Evaluation & Safety
08Multimodal & Applications
09Classical ML & Statistical Learning
10Causal Inference & Experimentation
11Time Series & Forecasting
12Graphs, Recommenders & Structured Data
15Search & Information Retrieval
11
Time Series & Forecasting
Data with an arrow of time, where shuffling the rows destroys the problem.
1tracks
4concepts
56cards
0.5hreading
Time Series Foundations Stationarity, autocorrelation, ARIMA and state space models, seasonality and honest backtesting. 4 concepts · 56 cards
- 01 Seasonality and Decomposition Splitting a series into trend, seasonal and remainder components, and the choice between additive and multiplicative structure that determines whether the seasonal pattern grows with the level.
- 02 Autocorrelation and ARIMA Reading the autocorrelation and partial autocorrelation functions to identify how much of a series is explained by its own past, and what the AR, I and MA components each represent.
- 03 Backtesting and Temporal Validation Random cross-validation on time series lets a model learn from the future, and the alternatives all trade honesty against how much of the data can be used.
- 04 Stationarity and Differencing Almost every time series method assumes the statistical properties do not change over time, and the transformations that enforce that assumption also change what the model is predicting.