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0.2.4 – Column Space and Nullspace
Solving the problem from the 7. recitation video from the MIT course “18.06SC – Linear Algebra”.
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0.2.3 – Column Space and Nullspace
Solving problem 6.3 from the MIT course “18.06SC – Linear Algebra”.
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0.2.2 – Column Space and Nullspace
Solving problem 6.2 from the MIT course “18.06SC – Linear Algebra”.
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0.2.1 – Column Space and Nullspace
Solving problem 6.1 from the MIT course “18.06SC – Linear Algebra”.
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0.2.0 – Polynomial Regression
Different datasets have different underlying structures, while some have linear underlying structures other can have nonlinear structures: The differences are notable. When applying linear regression to both cases, it is apparent that linear regression is not a good way to model the nonlinear dataset. To capture the non-linearity better we utilize the fact that, in…
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0.1.1 – Transposes, Permutations, Vector Spaces
Solving the problem from the 6. recitation video from the MIT course “18.06SC – Linear Algebra”.
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0.1.0 – Linear Regression
Linear regression is one of the oldest and most fundamental forms of linear modelling, developed/discovered by none other than Carl Fredriech Gauss. Much of modern machine learning is just a beefed up version of linear regression so its worth understanding it well. Linear regression is used to approximate the best solution to a system of…
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0.0.17 – Transposes, Permutations, Vector Spaces
Solving problem 5.3 from the MIT course “18.06SC – Linear Algebra”.
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0.0.16 – Transposes, Permutations, Vector Spaces
Solving problem 5.2 from the MIT course “18.06SC – Linear Algebra”.
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0.0.15 – Transposes, Permutations, Vector Spaces
Solving problem 5.1 from the MIT course “18.06SC – Linear Algebra”.
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0.0.14 – Factorization into A = LU
Solving the problem from the 4. recitation video from the MIT course “18.06SC – Linear Algebra”.
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0.0.13 – Factorization into A = LU
Solving problem 4.2 from the MIT course “18.06SC – Linear Algebra”.
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0.0.12 – Factorization into A = LU
Solving problem 4.1 from the MIT course “18.06SC – Linear Algebra”.
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0.0.11 – Multiplication and Inverse Matrices
Solving the problem from the 4. recitation video from the MIT course “18.06SC – Linear Algebra”.
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0.0.10 – Elimination with Matrices
Solving the problem from the 3. recitation video from the MIT course “18.06SC – Linear Algebra”.
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0.0.9 – Multiplication and Inverse Matrices
Solving problem 3.2 from the MIT course “18.06SC – Linear Algebra”.
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0.0.8 – Multiplication and Inverse Matrices
Solving problem 3.1 from the MIT course “18.06SC – Linear Algebra”.
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0.0.7 – Elimination with Matrices
Solving problem 2.2 from the MIT course “18.06SC – Linear Algebra”.
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0.0.6 – Elimination with Matrices
Solving problem 2.1 from the MIT course “18.06SC – Linear Algebra”.
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0.0.5 – The Geometry of Linear Equations
Solving the problem from the 1. recitation video from the MIT course “18.06SC – Linear Algebra”.
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