Linear algebra has a history dating back to the 17th century, with matrices originally used to store sets of numbers like geometric coordinates and systems of equations. In the 20th century, matrices and vectors were applied to multivariate mathematics, physics, economics and more. Modern linear algebra is computational and taught through coding applications in graphics, statistics, data science and AI, as computers efficiently work with matrices. Vectors are ordered lists of numbers that can be added by summing corresponding elements, and the dot product of vectors can be calculated using NumPy's np.dot() function.

1. linear
algebra
By: Mohamed Essam

2. Linear algebra
Linear algebra has an interesting history in mathematics, dating
back to the 17th century in the West and much earlier in China.
Matrices—the spreadsheets of numbers at the heart of linear
algebra .
were used to provide a compact notation for storing sets of
numbers like geometric coordinates (this was Descartes’ original
use of matrices) and systems of equations (pioneered by Gauss).
In the 20th century, matrices and vectors were used for
multivariate mathematics including calculus, differential
equations, physics, and economics.

3. Linear algebra
But most people didn’t need to care about matrices until fairly
recently. Here’s the thing: Computers are extremely efficient at
working with matrices. And so, modern computing gave rise to
modern linear algebra. Modern linear algebra is computational
whereas traditional linear algebra is abstract. Modern linear
algebra is best learned through code and applications in
graphics, statistics, data science, A.I.,

4. Vectors
Creating and visualizing vectors in numpy
In linear algebra, a vector is an ordered list of numbers. (In abstract linear
algebra, vectors may contain other mathematical objects including
functions;
Dimensionality: The number of numbers in the vector.
Orientation: Whether the vector is in column orientation (standing up
tall) or row orientation (laying flat and wide).

6. Vectors-Adding two vectors
To add two vectors, simply add each corresponding element. Here is an
example:

7. Vectors-Adding two vectors
To add two vectors, simply add each corresponding element. Here is an
example:

8. Vectors-Adding two vectors
To add two vectors, simply add each corresponding element. Here is an
example:

9. Vectors- Dot prodcut
There are multiple ways to implement the dot product in Python; the most
straightforward way is to the use the np.dot() function.

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