Jupyter notebooks that draw the math behind deep learning

What it does

This repository is a set of Jupyter Notebooks that unpack Chapter 2 of the Deep Learning Book by Goodfellow, Bengio, and Courville. Each notebook pairs the chapter’s mathematical concepts with Python/Numpy code and custom plots, translating abstract linear algebra into runnable, visual examples. It functions as a guided companion for readers who find the original text too terse.

The interesting bit

Instead of treating matrices as inert spreadsheets, the author frames them as spatial transformations and uses plots to show how operations like the dot product or eigendecomposition deform space. The project is essentially a pedagogical layer on top of existing material, not a new library or framework.

Key highlights

• Covers the full Chapter 2 syllabus, from scalars and tensors through PCA.
• Heavy emphasis on graphical intuition: plots for norms, linear dependence, eigendecomposition, and SVD.
• Every concept is mirrored with Numpy/Scipy/Matplotlib code you can experiment with.
• Includes a bonus SVD image-processing demo featuring “Lucy the goose.”
• Explicitly aimed at beginners who need intermediate steps the textbook skips.

Caveats

• Only covers Chapter 2; the rest of the Deep Learning Book is untouched.
• Content reflects the author’s personal understanding, so it is a companion, not an official replacement or errata.

Verdict

Worth bookmarking if you are a beginner or advanced beginner struggling to visualize the linear algebra that underpins deep learning. Skip it if you already read math notation fluently or need tools rather than tutorials.