updated svd denoising

README.md

@@ -6,7 +6,6 @@ This repository demonstrates how core linear algebra concepts -- least squares,
 
 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.
 
----
 
 ## Structure
 
@@ -22,6 +21,27 @@ LICENSE           # project license
 
 Each notebook is self-contained and moves from theory to implementation to visualization.
 
+## Dependencies
+
+* **Python 3**
+* **NumPy** -- linear algebra
+* **Pandas** -- data handling
+* **Matplotlib** -- visualization
+* **Pillow** -- imaging library
+
+## How to Run
+
+```bash
+git clone https://gitlab.com/psark/ds-for-la.git
+cd ds-for-la
+
+pip install requirements.txt
+
+jupyter notebook
+```
+
+Open any notebook inside the `notebooks/` folder.
+
 ---
 
 ## Topics
@@ -83,16 +103,6 @@ For color images, this is applied independently to each channel (R, G, B).
 
 ---
 
-## Dependencies
-
-* **Python 3**
-* **NumPy** -- linear algebra
-* **Pandas** -- data handling
-* **Matplotlib** -- visualization
-* **Pillow** -- imaging library
-
----
-
 ## Key Takeaways
 
 * Data science problems can be framed as:
@@ -111,20 +121,6 @@ For color images, this is applied independently to each channel (R, G, B).
   * compression
   * interpretability
 
----
-
-## How to Run
-
-```bash
-git clone <your-repo-url>
-cd data-science-linear-algebraist
-
-pip install requirements.txt
-
-jupyter notebook
-```
-
-Open any notebook inside the `notebooks/` folder.
 
 ---
 
@@ -132,7 +128,6 @@ Open any notebook inside the `notebooks/` folder.
 
 This project is part of a broader effort to translate a background in pure mathematics into practical data science and machine learning skills.
 
----
 
 ## Future Work
 

requirements.txt

@@ -2,3 +2,4 @@ pandas
 numpy
 matplotlib
 pillow
+scikit-image