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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 01-Topics.en.dfxp |
1.31Кб |
| 01-Topics.mp4 |
40.43Мб |
| 02-Topics.en.dfxp |
1.54Кб |
| 02-Topics.mp4 |
45.02Мб |
| 03-3.1 Tensor Transposition.en.dfxp |
6.73Кб |
| 03-3.1 Tensor Transposition.mp4 |
92.22Мб |
| 04-3.5 Exercises.en.dfxp |
9.56Кб |
| 04-3.5 Exercises.mp4 |
416.55Мб |
| 05-3.2 Basic Tensor Arithmetic.en.dfxp |
10.99Кб |
| 05-3.2 Basic Tensor Arithmetic.mp4 |
143.94Мб |
| 06-Topics.en.dfxp |
1014б |
| 06-Topics.mp4 |
29.79Мб |
| 07-5.4 Exercises.en.dfxp |
14.51Кб |
| 07-5.4 Exercises.mp4 |
629.77Мб |
| 08-6.4 Orthogonal Matrices.en.dfxp |
8.31Кб |
| 08-6.4 Orthogonal Matrices.mp4 |
126.01Мб |
| 09-6.2 Matrix Inversion.en.dfxp |
28.72Кб |
| 09-6.2 Matrix Inversion.mp4 |
304.81Мб |
| 10-Topics.en.dfxp |
1.04Кб |
| 10-Topics.mp4 |
32.22Мб |
| 11-Topics.en.dfxp |
1.63Кб |
| 11-Topics.mp4 |
53.46Мб |
| 12-1.1 Defining Linear Algebra.en.dfxp |
11.83Кб |
| 12-1.1 Defining Linear Algebra.mp4 |
168.05Мб |
| 13-1.5 Exercise.en.dfxp |
14.93Кб |
| 13-1.5 Exercise.mp4 |
651.21Мб |
| 14-4.1 The Substitution Strategy.en.dfxp |
6.62Кб |
| 14-4.1 The Substitution Strategy.mp4 |
270.70Мб |
| 15-4.2 Substitution Exercises.en.dfxp |
13.21Кб |
| 15-4.2 Substitution Exercises.mp4 |
641.60Мб |
| 16-4.4 Elimination Exercises.en.dfxp |
15.58Кб |
| 16-4.4 Elimination Exercises.mp4 |
725.60Мб |
| 17-Topics.en.dfxp |
1.49Кб |
| 17-Topics.mp4 |
49.50Мб |
| 18-5.3 Symmetric and Identity Matrices.en.dfxp |
9.31Кб |
| 18-5.3 Symmetric and Identity Matrices.mp4 |
121.07Мб |
| 19-7.4 High-Dimensional Eigenvectors.en.dfxp |
7.40Кб |
| 19-7.4 High-Dimensional Eigenvectors.mp4 |
142.51Мб |
| 20-1.2 Solving a System of Equations Algebraically.en.dfxp |
12.98Кб |
| 20-1.2 Solving a System of Equations Algebraically.mp4 |
115.00Мб |
| 21-Linear Algebra for Machine Learning (Machine Learning Foundations) - Introduction.en.dfxp |
6.62Кб |
| 21-Linear Algebra for Machine Learning (Machine Learning Foundations) - Introduction.mp4 |
214.07Мб |
| 22-1.3 Linear Algebra in Machine Learning and Deep Learning.en.dfxp |
18.95Кб |
| 22-1.3 Linear Algebra in Machine Learning and Deep Learning.mp4 |
268.86Мб |
| 23-2.1 Tensors.en.dfxp |
7.72Кб |
| 23-2.1 Tensors.mp4 |
98.51Мб |
| 24-2.7 Generic Tensor Notation.en.dfxp |
8.96Кб |
| 24-2.7 Generic Tensor Notation.mp4 |
198.67Мб |
| 25-2.3 Vectors and Vector Transposition.en.dfxp |
19.10Кб |
| 25-2.3 Vectors and Vector Transposition.mp4 |
285.21Мб |
| 26-Topics.en.dfxp |
844б |
| 26-Topics.mp4 |
25.18Мб |
| 27-3.3 Reduction.en.dfxp |
8.35Кб |
| 27-3.3 Reduction.mp4 |
90.88Мб |
| 28-2.4 Norms and Unit Vectors.en.dfxp |
29.56Кб |
| 28-2.4 Norms and Unit Vectors.mp4 |
321.34Мб |
| 29-2.5 Basis, Orthogonal, and Orthonormal Vectors.en.dfxp |
8.42Кб |
| 29-2.5 Basis, Orthogonal, and Orthonormal Vectors.mp4 |
80.91Мб |
| 30-4.3 The Elimination Strategy.en.dfxp |
6.48Кб |
| 30-4.3 The Elimination Strategy.mp4 |
307.80Мб |
| 31-5.2 Matrix-by-Matrix Multiplication.en.dfxp |
18.60Кб |
| 31-5.2 Matrix-by-Matrix Multiplication.mp4 |
598.93Мб |
| 32-5.1 Matrix-by-Vector Multiplication.en.dfxp |
20.16Кб |
| 32-5.1 Matrix-by-Vector Multiplication.mp4 |
756.66Мб |
| 33-3.4 The Dot Product.en.dfxp |
11.69Кб |
| 33-3.4 The Dot Product.mp4 |
153.97Мб |
| 34-5.5 Machine Learning and Deep Learning Applications.en.dfxp |
22.56Кб |
| 34-5.5 Machine Learning and Deep Learning Applications.mp4 |
325.27Мб |
| 35-6.1 The Frobenius Norm.en.dfxp |
6.47Кб |
| 35-6.1 The Frobenius Norm.mp4 |
111.89Мб |
| 36-7.1 The Eigenconcept.en.dfxp |
15.25Кб |
| 36-7.1 The Eigenconcept.mp4 |
402.70Мб |
| 37-6.3 Diagonal Matrices.en.dfxp |
6.93Кб |
| 37-6.3 Diagonal Matrices.mp4 |
125.65Мб |
| 38-7.2 Exercises.en.dfxp |
14.45Кб |
| 38-7.2 Exercises.mp4 |
700.68Мб |
| 39-8.1 The Determinant of a 2 x 2 Matrix.en.dfxp |
11.09Кб |
| 39-8.1 The Determinant of a 2 x 2 Matrix.mp4 |
146.54Мб |
| 40-Topics.en.dfxp |
1.39Кб |
| 40-Topics.mp4 |
46.27Мб |
| 41-8.3 Exercises.en.dfxp |
7.71Кб |
| 41-8.3 Exercises.mp4 |
294.38Мб |
| 42-8.2 The Determinants of Larger Matrices.en.dfxp |
14.99Кб |
| 42-8.2 The Determinants of Larger Matrices.mp4 |
141.91Мб |
| 43-8.4 Determinants and Eigenvalues.en.dfxp |
14.56Кб |
| 43-8.4 Determinants and Eigenvalues.mp4 |
177.48Мб |
| 44-9.4 Regression via Pseudoinversion.en.dfxp |
24.41Кб |
| 44-9.4 Regression via Pseudoinversion.mp4 |
288.04Мб |
| 45-9.2 Media File Compression.en.dfxp |
12.21Кб |
| 45-9.2 Media File Compression.mp4 |
138.60Мб |
| 46-9.1 Singular Value Decomposition.en.dfxp |
14.29Кб |
| 46-9.1 Singular Value Decomposition.mp4 |
194.53Мб |
| 47-9.3 The Moore-Penrose Pseudoinverse.en.dfxp |
17.56Кб |
| 47-9.3 The Moore-Penrose Pseudoinverse.mp4 |
240.44Мб |
| 48-9.6 Resources for Further Study of Linear Algebra.en.dfxp |
7.42Кб |
| 48-9.6 Resources for Further Study of Linear Algebra.mp4 |
145.43Мб |
| 49-1.4 Historical and Contemporary Applications.en.dfxp |
14.09Кб |
| 49-1.4 Historical and Contemporary Applications.mp4 |
252.57Мб |
| 50-2.2 Scalars.en.dfxp |
42.32Кб |
| 50-2.2 Scalars.mp4 |
990.11Мб |
| 51-2.6 Matrices.en.dfxp |
14.26Кб |
| 51-2.6 Matrices.mp4 |
189.87Мб |
| 52-2.8 Exercises.en.dfxp |
3.74Кб |
| 52-2.8 Exercises.mp4 |
85.27Мб |
| 53-Topics.en.dfxp |
943б |
| 53-Topics.mp4 |
29.86Мб |
| 54-6.5 The Trace Operator.en.dfxp |
7.30Кб |
| 54-6.5 The Trace Operator.mp4 |
114.65Мб |
| 55-7.3 Eigenvectors in Python.en.dfxp |
57.35Кб |
| 55-7.3 Eigenvectors in Python.mp4 |
700.92Мб |
| 56-8.5 Eigendecomposition.en.dfxp |
25.78Кб |
| 56-8.5 Eigendecomposition.mp4 |
443.35Мб |
| 57-Linear Algebra for Machine Learning (Machine Learning Foundations) - Summary.en.dfxp |
2.50Кб |
| 57-Linear Algebra for Machine Learning (Machine Learning Foundations) - Summary.mp4 |
86.41Мб |
| 58-9.5 Principal Component Analysis.en.dfxp |
13.55Кб |
| 58-9.5 Principal Component Analysis.mp4 |
207.29Мб |
| TutsNode.com.txt |
63б |