Jupyter Notebooks
Notebooks are the lab bench of AI engineering. You prototype here, then move what works into production. Install and launch JupyterLab, Jupyter Notebook, or VS Code with the Jupyter extension. Use magic commands (%timeit, %%time, %matplotlib inline) to benchmark and visualize inline. Distinguish when to use notebooks vs scripts and apply the "explore in notebooks, ship in scripts" workflow. Identify and avoid common notebook traps: out-of-order execution, hidden state, and memory leaks. Every AI paper, tutorial, and Kaggle competition uses Jupyter notebooks. They let you run code in pieces, see outputs inline, mix code with explanations, and iterate fast. If you try to learn AI without notebooks, you're doing math homework without scratch paper. But notebooks have real traps. People use them for everything, including things they're terrible at. Knowing when to use a notebook and when to use a script will save you from debugging nightmares later. A notebook is a list of cells. Each cell is either code or text. The kernel is a Python process running in the background. When you run a cell, it sends the code to the kernel, which executes it and sends back the result. All cells share the same kernel, so variables persist between cells. That "whatever order you click" part is both the superpower and the foot-gun. Three options, one format:…
Jupyter Notebooks: Notebooks are the lab bench of AI engineering. You prototype here, then move what works into production. Install and launch JupyterLab,…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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