🧭 Roadmap β€Ί 04 Β· Build Language Models β€Ί Pretraining β€Ί Training Data
← Pretraining

Training Data

πŸ”΄ Core
Area04 Β· Build Language Models
TopicPretraining
Topic priorityπŸ”΄ MUST
Subtopic1 of 11
Study this using 6 phases
🧠 01 Understand

What is Training Data? Why does it exist? Where does it appear in Pretraining?

πŸ‘οΈ 02 Visualize

Draw or describe a visual model of Training Data. What does it look like conceptually?

✏️ 03 Calculate

Work through a concrete manual example of Training Data with real numbers or pseudocode.

πŸ’» 04 Code

Implement Training Data from scratch in Python. Keep it minimal β€” just enough to see it work.

βš–οΈ 06 Compare

How is Training Data different from related concepts in Pretraining? What are the trade-offs?

πŸ—£οΈ 07 Explain

Explain Training Data in plain language using an analogy. Could a non-expert follow it?

Dataset Preparation β†’
πŸ“‚ Real Dataset Sources β€” Where to Find Training Data

When you start experimenting, these are the places to get real data β€” from classic tabular datasets for understanding fundamentals, all the way up to LLM-scale web corpora:

πŸ›οΈ
UCI Machine Learning Repository
650+ classic datasets (Iris, Heart Disease, Wine…) β€” perfect for hands-on concept practice Β· archive.ics.uci.edu
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Hugging Face Datasets
70k+ NLP / LLM datasets, loadable in one Python line Β· huggingface.co/datasets
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🌐
Common Crawl
Petabyte-scale web text β€” the backbone of GPT-3, LLaMA pre-training Β· commoncrawl.org
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πŸ“‹ See full Datasets & Resources page β†’
πŸ§ͺ
RASL Pull Β· S-05 Pulled because: how do checkpoints work β€” what does save/restore mean?
πŸ”¨ M-06 train.py checkpoints πŸ” checkpoint pattern = RASL state snapshots β†’ return to train.py πŸ“‹ full plan β†’
↑ All subtopics in Pretraining
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