[DesireCourse.Net] Udemy - Mathematical Foundation For Machine Learning and AI

• [DesireCourse.Net] Udemy - Mathematical Foundation For Machine Learning and AI
• 1. Introduction/1. Introduction.mp4 27.8 MB
• 2. Linear Algebra/1. Scalars, Vectors, Matrices, and Tensors.mp4 215.5 MB
• 2. Linear Algebra/2. Vector and Matrix Norms.mp4 53.1 MB
• 2. Linear Algebra/3. Vectors, Matrices, and Tensors in Python.mp4 114 MB
• 2. Linear Algebra/3.1 Project 1 - Vectors, Matrices, and Tensors in Python.zip.zip 176.6 KB
• 2. Linear Algebra/4. Special Matrices and Vectors.mp4 121.1 MB
• 2. Linear Algebra/5. Eigenvalues and Eigenvectors.mp4 64.3 MB
• 2. Linear Algebra/6. Norms and Eigendecomposition.mp4 177.9 MB
• 2. Linear Algebra/6.1 Project 2 - Norms and Eigendecomposition.zip.zip 232 KB
• 3. Multivariate Calculus/1. Introduction to Derivatives.mp4 143.3 MB
• 3. Multivariate Calculus/2. Basics of Integration.mp4 53.1 MB
• 3. Multivariate Calculus/3. Gradients.mp4 76.8 MB
• 3. Multivariate Calculus/4. Gradient Visualization.mp4 105.5 MB
• 3. Multivariate Calculus/4.1 Project 1 - Gradient Visualization.zip.zip 513 KB
• 3. Multivariate Calculus/5. Optimization.mp4 233.1 MB
• 4. Probability Theory/1. Intro to Probability Theory.mp4 96.8 MB
• 4. Probability Theory/2. Probability Distributions.mp4 79.2 MB
• 4. Probability Theory/3. Expectation, Variance, and Covariance.mp4 55.4 MB
• 4. Probability Theory/4. Graphing Probability Distributions in R.mp4 90.5 MB
• 4. Probability Theory/4.1 Graphing Probability Distributions in R.zip.zip 1.1 KB
• 4. Probability Theory/5. Covariance Matrices in R.mp4 61.7 MB
• 4. Probability Theory/5.1 Covariance Matrices in R.zip.zip 787 B
• 5. Probaility Theory/1. Special Random Variables.mp4 71.4 MB
• 5. Probaility Theory/1.1 Special Random Variables.zip.zip 133.5 KB
• 5. Probaility Theory/2. Bonus Lecture More Interesting Stuff, Offers and Discounts.html 1.7 KB

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