Build an LLM from Scratch 2: Working with text data @SebastianRaschka
Build an LLM from Scratch 2: Working with text data  @SebastianRaschka
Uploaded March 2025 | Updated September 2026, 2 weeks ago
Links to the book:
- amzn.to/4fqvn0D (Amazon)
- https://mng.bz/M96o (Manning)

Link to the GitHub repository: github.com/rasbt/LLMs-from-scratch

This is a supplementary video going over text data preparations steps (tokenization, byte pair encoding, data loaders, etc.) for LLM training.

00:00 2.2 Tokenizing text
14:02 2.3 Converting tokens into token IDs
23:56 2.4 Adding special context tokens
30:26 2.5 Byte pair encoding
44:00 2.6 Data sampling with a sliding window
1:07:10 2.7 Creating token embeddings
1:15:45 2.8 Encoding word positions

You can find additional bonus materials on GitHub:

Byte Pair Encoding (BPE) Tokenizer From Scratch, github.com/rasbt/LLMs-from-scratch/blob/main/ch02/05_bpe-from-scratch/bpe-from-scratch.ipynb

Comparing Various Byte Pair Encoding (BPE) Implementations, github.com/rasbt/LLMs-from-scratch/blob/main/ch02/02_bonus_bytepair-encoder/compare-bpe-tiktoken.ipynb

Understanding the Difference Between Embedding Layers and Linear Layers, github.com/rasbt/LLMs-from-scratch/blob/main/ch02/03_bonus_embedding-vs-matmul/embeddings-and-linear-layers.ipynb

Data sampling with a sliding window with number data, github.com/rasbt/LLMs-from-scratch/blob/main/ch02/04_bonus_dataloader-intuition/dataloader-intuition.ipynb

A video on the effect of random seeds: youtube.com/watch?v=ii89_SqKB08&feature=youtu.be
Build an LLM from Scratch 2: Working with text dataL16.4 A Convolutional Autoencoder in PyTorch   Code ExampleL11.2 How BatchNorm WorksL4.0 Linear Algebra for Deep Learning   Lecture OverviewL4.3 Vectors, Matrices, and BroadcastingL5.2 Relation Between Perceptron and Linear RegressionL10.5.3 (Optional) Dropout Ensemble InterpretationL8.7.1 OneHot Encoding and Multi-category Cross EntropyL4.4 Notational Conventions for Neural NetworksBuild an LLM from Scratch 7: Instruction FinetuningBuild an LLM from Scratch 6: Finetuning for ClassificationL8.7.2 OneHot Encoding and Multi-category Cross Entropy   Code Example
Sebastian Raschka |

Build an LLM from Scratch 2: Working with text data

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