Fix: Set notebook version of library to latest (#57)

* fix: set version of notebooks automatically to latest

* chore: added linear_regression tutorial notebook

* chore: added linear_regression to README.md

Author: jcabrero

examples/README.md

@@ -6,3 +6,4 @@ The following are the currently available examples:
 - **Matrix Multiplication** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NillionNetwork/nada-numpy/blob/main/examples/matrix_multiplication/matrix_multiplication.ipynb)
 - **Broadcasting** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NillionNetwork/nada-numpy/blob/main/examples/broadcasting/broadcasting.ipynb)
 - **Rational Numbers** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NillionNetwork/nada-numpy/blob/main/examples/rational_numbers/rational_numbers.ipynb)
+- **[Advanced] Linear Regression Closed-Form Training** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NillionNetwork/nada-numpy/blob/main/examples/linear_regression/linear_regression.ipynb)

examples/broadcasting/broadcasting.ipynb

@@ -60,7 +60,7 @@
       },
       "outputs": [],
       "source": [
-        "%pip install nada-numpy~=0.3.0 --quiet"
+        "%pip install nada-numpy --quiet"
       ]
     },
     {

examples/dot_product/dot_product.ipynb

@@ -81,7 +81,7 @@
         }
       ],
       "source": [
-        "%pip install nada-numpy~=0.3.0 --quiet"
+        "%pip install nada-numpy --quiet"
       ]
     },
     {

examples/linear_regression/README.md

@@ -0,0 +1,25 @@
+# Linear Regression Closed-Form Solution Tutorial
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NillionNetwork/nada-numpy/blob/main/examples/linear_regression/linear_regression.ipynb)
+
+This tutorial shows how to train a linear regression privately based on the closed-form solution to linear regression.
+
+This is an implementation on the [paper by Blom et al.](https://eprint.iacr.org/2019/773.pdf)
+
+## 🚨 Limitations
+The choice for blind computing implies certain trade-offs in comparison to conventional computing. What you gain in privacy, you pay in extra computational overhead & capacity constraints.
+
+Therefore, you will notice that large-scale computational workloads may lead to long compilation and/or execution times or hitting network capacity guardrails.
+
+That said, the Nillion team is working around the clock to push the boundaries of this technology and bring the potential of blind computing to reality 🚀
+
+👉 This example has been tested on local devnet with the current given dimension of 3 x 3.
+
+## ➡️ Stay in touch
+If you want to get involved in the blind computing community and be the first to know all big updates, join our Discord
+
+[![Discord](https://img.shields.io/badge/Discord-nillionnetwork-%235865F2?logo=discord)](https://discord.gg/nillionnetwork)
+
+And if you want to contribute to the blind computing revolution, we welcome open-source contributors!
+
+[![GitHub Discussions](https://img.shields.io/badge/GitHub_Discussions-NillionNetwork-%23181717?logo=github)](https://github.com/orgs/NillionNetwork/discussions)