Update tech_docs/math_objects_python.md

tech_docs/math_objects_python.md

@@ -1,4 +1,4 @@
-  # Mathematical Objects and Their Applications in Python
+# Mathematical Objects and Their Applications in Python
 
 This document explains different types of mathematical objects and their applications. Additionally, it provides information on Python libraries that can be used to work with these objects.
 
@@ -7,7 +7,9 @@ This document explains different types of mathematical objects and their applica
 A **scalar** is a single number, typically used in mathematics to represent a magnitude or quantity. In programming and data science, scalars are often used as individual data points or constants.
 
 ### Example
+```latex
 $$ a = 3 $$
+```
 
 ### Python Libraries
 - **NumPy**: Used for handling numerical operations; scalars can be represented as `numpy.float64` or `numpy.int32`, etc.
@@ -17,7 +19,9 @@ $$ a = 3 $$
 A **vector** is an ordered array of numbers, which can represent direction and magnitude in space. Vectors are fundamental in physics for describing velocities and forces, in engineering for load distributions, and in machine learning for feature representation.
 
 ### Example
+```latex
 $$ \mathbf{v} = \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} $$
+```
 
 ### Python Libraries
 - **NumPy**: Provides a powerful array structure that can be used to represent vectors.
@@ -28,7 +32,9 @@ $$ \mathbf{v} = \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} $$
 A **matrix** is a two-dimensional array of numbers used extensively in engineering, physics, computer science, and statistics. Matrices are crucial in solving systems of linear equations, transforming geometric data, and in algorithms for machine learning and artificial intelligence.
 
 ### Example
+```latex
 $$ \mathbf{M} = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} $$
+```
 
 ### Python Libraries
 - **NumPy**: Essential for creating and manipulating matrices.
@@ -40,12 +46,14 @@ $$ \mathbf{M} = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} $$
 A **tensor** extends the concept of vectors and matrices to potentially higher dimensions. In the field of machine learning and neural networks, tensors are key structures, representing complex datasets and the parameters of the models themselves.
 
 ### Example
+```latex
 $$ \mathcal{T} = \begin{bmatrix} 
 \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} & 
 \begin{bmatrix} 5 & 6 \\ 7 & 8 \end{bmatrix} \\
 \begin{bmatrix} 9 & 10 \\ 11 & 12 \end{bmatrix} & 
 \begin{bmatrix} 13 & 14 \\ 15 & 16 \end{bmatrix}
 \end{bmatrix} $$
+```
 
 ### Python Libraries
 - **TensorFlow**: A library designed specifically for working with tensors in neural networks.
@@ -54,4 +62,4 @@ $$ \mathcal{T} = \begin{bmatrix}
 
 ## Conclusion
 
-Understanding these basic mathematical objects is crucial for working effectively in fields such as data science, AI, physics, and engineering. Python, with its rich ecosystem of libraries, provides extensive support for manipulating these objects, making it a preferred language for scientific and engineering applications.
+Understanding these basic mathematical objects is crucial for working effectively in fields such as data science, AI, physics, and engineering. Python, with its rich ecosystem of libraries, provides extensive support for manipulating these objects, making it a preferred language for scientific and engineering applications.