Build an LLM from Scratch 1: Set up your code environment @SebastianRaschka
Build an LLM from Scratch 1: Set up your code environment  @SebastianRaschka
Uploaded February 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 explaining how to set up a Python environment using uv.

00:00 Introduction
01:33 Setup info on GitHub
03:30 Optional setup preferences and uv
05:00 uv pip vs uv add syntax
05:35 1) Installing Python
09:05 2) Setting up uv
10:12 3) Creating a virtual environment
14:03 4) Installing packages
16:34 5) pip install fallback
16:57 6) If nothing works: Google Colab :)
19:07 7) uv run to run Jupyter Lab locally

In particular, we are using `uv pip`, which is explained in this document: github.com/rasbt/LLMs-from-scratch/blob/main/setup/01_optional-python-setup-preferences/README.md

Alternatively, the native `uv add` syntax (mentioned but not explicitly covered in this video) is described here: github.com/rasbt/LLMs-from-scratch/blob/main/setup/01_optional-python-setup-preferences/native-uv.md
Build an LLM from Scratch 1: Set up your code environment13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)L13.9.1 LeNet-5 in PyTorchL17.4 Variational Autoencoder Loss FunctionL9.3.1 Multilayer Perceptron   Code Example Part 1/3 (Slide Overview)L4.2 Tensors in PyTorch
Sebastian Raschka |

Build an LLM from Scratch 1: Set up your code environment

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