Python NumPy (Prototype Project 01) - Intro to Linear Algebra
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00:05 demo a prebuilt version of the application
00:21 add 2 matrices (both must have same size. add elements in same position)
01:04 subtract 2 matrices (both must have same size. subtract elements in same position)
01:30 multiply 2 matrices (both must have matching inner dimension. size of resulting matrix obtained by dropping middle dimension)
02:19 step by step example of multiplying 2 matrices
04:10 scalar add (operation applied to each element in matrix)
04:44 scalar subtract (operation applied to each element in matrix)
05:04 scalar multiply (operation applied to each element in matrix)
05:22 scalar divide (operation applied to each element in matrix)
05:42 identity matrix contains all 0s except diagonal is 1s (multiply matrix by identity matrix and obtain original matrix)
06:45 transpose matrix by flipping it along diagonal. rows become columns and columns become rows
07:20 dot product happens between 2 vectors (here we do element-wise multiplication than sum up results)
08:54 NumPy setup using miniconda
09:22 code the application
09:26 represent matrix as 2 dimensional array
09:39 add and subtract matrices in NumPy
09:58 multiply matrices in NumPy
10:07 matrix scalar operations in NumPy
10:33 identity matrix operation in NumPy
10:55 transpose matrix operation in NumPy
11:01 dot product matrix operation in NumPy
11:25 vector matrix
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