Fix: minor tutorial typos

README.md

@@ -47,7 +47,7 @@ Join the community and help build a safer and transparent AI in our [Discord](ht
 
 ## 🚀 Orion Usage
 - For an insightful overview of impressive proof of concepts, models, and tutorials created by our community, please visit [Orion Usage](https://github.com/gizatechxyz/orion/blob/main/orion-usage.md).
-- Discover a currated list of tutorials and models developed using Orion in [Orion-Hub](https://github.com/gizatechxyz/Orion-Hub).
+- Discover a curated list of tutorials and models developed using Orion in [Orion-Hub](https://github.com/gizatechxyz/Orion-Hub).
 
 ## ✍️ Authors & contributors
 

docs/academy/tutorials/verifiable-linear-regression-model-in-orion.md

@@ -24,7 +24,7 @@ $$
 
 #### Generating the dataset
 
-In the following [notebook](https://github.com/gizatechxyz/orion\_tutorials/tree/main/verifiable\_linear\_regression\_model), we will create a synthetic dataset that will serve as the backbone throughout our tutorial.
+In the following [notebook](https://github.com/gizatechxyz/orion\_tutorials/tree/main/basic/verifiable\_linear\_regression\_model), we will create a synthetic dataset that will serve as the backbone throughout our tutorial.
 
 ```python
 import numpy as np
@@ -152,7 +152,7 @@ test = "scarb cairo-test -f linear_regression_test"
 
 ```
 
-#### Gerating the dataset in Cairo
+#### Generating the dataset in Cairo
 
 Now let’s generate the files required to begin our transition to Cairo. In our Jupyter Notebook, we will execute the code required to turn our synthetic dataset to fixed point values and represent our X and y values as Fixedpoint Tensors in Orion.
 

docs/academy/tutorials/verifiable-principal-components-analysis.md

@@ -302,7 +302,7 @@ version = "0.1.0"
 orion = { git = "https://github.com/gizatechxyz/orion.git", rev = "v0.1.7" }
 ```
 
-#### Gerating the dataset in Cairo
+#### Generating the dataset in Cairo
 
 Now let's generate the necessary files to begin our transition to Cairo. In our Jupyter Notebook, we will run the necessary code to convert our iris dataset obtained from sklearn.datasets into fixed point values and represent our X, and y values as fixed point tensors in Orion. For the purposes of the tutorial, we will work directly with the Xstd data obtained from python, so we will also convert these to fixed point values.
 

docs/academy/tutorials/verifiable-support-vector-machine.md

@@ -3,7 +3,7 @@
 <figure><img src="../../.gitbook/assets/orion_banner_tutorial_#4.png" alt=""><figcaption></figcaption></figure>
 
 {% hint style="info" %}
-Repository and Notebooks can be found [here](https://github.com/gizatechxyz/orion\_tutorials/tree/main/verifiable\_support\_vector\_machine).
+Repository and Notebooks can be found [here](https://github.com/gizatechxyz/orion_tutorials/tree/main/basic/verifiable_support_vector_machine).
 {% endhint %}
 
 The Support Vector Machines (SVM) model is a supervised learning technique used for classification and regression. It is employed to solve binary classification problems where it identifies the hyperplane that best divides a data set into classes. This hyperplane results from maximizing the margin between the two classes. By determining this optimal hyperplane, predictions can be made for new data points and understand how the input attributes influence classification.
@@ -177,7 +177,7 @@ version = "0.1.0"
 orion = { git = "https://github.com/gizatechxyz/orion.git", rev = "v0.1.0" }
 ```
 
-### Gerating the dataset in Cairo
+### Generating the dataset in Cairo
 
 Now let's generate the necessary files to begin our transition to Cairo. In our Jupyter Notebook, we'll run the necessary code to convert our dataset obtained with make\_blobs from Scikit-learn into fixed-point values and represent our X\_train, y\_train, X\_test, and y\_test values as fixed-point tensors in Orion.