LLMs from scratch
What actually happens inside a transformer when you send a prompt, and why the hard parts — hallucination, prompt fragility, and the rest — keep turning out to be architectural rather than bugs.
Start here
Where to start
A few hand-picked reading paths grouped by what you might want to learn. Pick the one closest to your goal and follow the sequence — each step links to a chapter, and each path also points to its full category if you want to dig further.
Building LLM agents
From calling the LLM to running it as part of a long-running system — how generation works, how to prompt it, where it silently fails, how to compose it into an agent, what the runtime around it needs to provide, and the rest of what production keeps demanding.
Fine-tuning LLMs
Start with the transformer/logit machinery, then follow the post-training path: when a weight delta is worth writing, which objective should create it, and which modern fine-tuning knobs matter once the stage is chosen.
Classical time-series forecasting
From the file format the data lives in through ARIMA, SARIMA, and the rest of the classical-statistics toolkit — the no-neural-networks route to time-series forecasting.
Deep learning for time-series forecasting
The deep-learning route to a time-series forecaster — from how the data is stored and shaped through the neural architectures (recurrent, convolutional, hybrid, and what comes next), the training recipes that make them converge, and the post-training lifecycle that keeps them useful in production.
- 01 Data Storage Formats
- 02 Time Series Data: Univariate, Multivariate, Panel, and Exogenous Variables
- 03 Time Series Preprocessing: Interpolation, Bucketing, Time Zones, and Missing Intervals
- 04 Deep Learning Architectures for Time Series
- 05 RNN and LSTM for Time-Series Forecasting
- 06 TCN: Causal and Dilated Convolutions for Time Series Forecasting
- 07 Playing with a Hybrid Architecture for Forecasting
- 08 Practical Training Recipes for Deep Learning Time Series
- 09 Interpretability and Production Maintenance for Deep Learning Time Series
Detecting anomalies in time series
Spotting outliers and shifts in time-series data — from the file formats and the data shapes through preprocessing to the statistical baselines that flag what doesn't belong, and the deeper detection methods that build on them.
Data storage and streaming
How data actually moves and lives in production: file formats, streaming queues, object stores, relational databases, and the other layers of the storage stack — what each is for and where they overlap.
Time in LLMs
How time keeps breaking LLMs — a training corpus that quietly knows the future of its own past, a runtime that can't tell five minutes from five hours between user turns, and the failure modes that fall out wherever the two meet.
Econometrics
Why a plain regression of outcome on treatment usually returns the wrong number, and the toolkit for fixing it — instrumental variables, regression discontinuity, fixed effects, control functions, and causal ML — built up around pricing, the worked example where every method earns its place.