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Fix typos and math errors in notebooks

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Pawel Sarkowicz
2026-05-27 09:01:22 -04:00
parent c95d440cdb
commit 894ea72db9
5 changed files with 25 additions and 25 deletions
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README.md
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@@ -4,9 +4,9 @@ A practical, linear-algebra-first introduction to data science.
This repository demonstrates how core linear algebra concepts -- least squares, matrix decompositions, and spectral methods -- directly power modern data science and machine learning workflows. We finish off with a mini-project involving image denoising using the truncated SVD.
Rather than treating data science as a collection of tools, this project builds everything from first principles and connects theory to implementation through jupyter notebooks.
Rather than treating data science as a collection of tools, this project builds everything from first principles and connects theory to implementation through Jupyter notebooks.
The compiled notebooks in this project can be viewed as a single webpage on my [website](https://pawelsarkowicz.xyz/posts/ds_for_la). Note that if you view in the notebooks in Gitlab/Github, they have a tendency to not render the latex properly.
The compiled notebooks in this project can be viewed as a single webpage on my [website](https://pawelsarkowicz.xyz/posts/ds_for_la). Note that if you view the notebooks in GitLab/GitHub, they have a tendency to not render the LaTeX properly.
## Structure
@@ -31,6 +31,7 @@ Each notebook is self-contained and moves from theory to implementation to visua
* **Matplotlib** -- visualization
* **Pillow** -- imaging library
* **scikit-learn** -- machine learning utilities
* **scikit-image** -- image quality metrics
## How to Run
@@ -38,7 +39,7 @@ Each notebook is self-contained and moves from theory to implementation to visua
git clone https://gitlab.com/psark/ds-for-la.git
cd ds-for-la
pip install requirements.txt
pip install -r requirements.txt
jupyter notebook
```
@@ -137,7 +138,7 @@ For color images, this is applied independently to each channel (R, G, B).
* Regularization connects directly to linear algebra:
* Ridge shifts singular values, improving condition number
* Lasso exploits $L^1$ geometry to product sparse solutions
* Lasso exploits $L^1$ geometry to produce sparse solutions
* Gradient descent convergence is governed by singular value structure
* Condition number determines learning rate stability
@@ -164,4 +165,4 @@ This project is part of a broader effort to translate a background in pure mathe
# License
This project is licensed under the MIT License.
See the [`LICENSE`](./LICENSE) file for details.
See the [`LICENSE`](./LICENSE) file for details.