Deep Learning, pt.1, Mathematics
Deep Learning course, part 1. This part of the course concentrates on the mathematical phenomena that occur in Neural Networks, both during training and inference. The main objective of this part is to demonstrate, how the training happens, and which mathematical concepts play an important role in it.
About
Content
- 1. Derivatives and Finite Differences
- 2. Derivatives Table
- 3. Deriving Derivatives lvl 1
- 4. Deriving Derivatives lvl 2
- 5. Deriving Derivatives lvl 3
- 6. Deriving Derivatives lvl 4
- 7. Derivatives Practice
- 11. Gradient Descent Practice
- 8. Gradient Descent
- 10. Rosenbrock Gradient Descent
- 9. Himmelblau Gradient Descent
- 1. Matrices Manual Exercise
- 2. Matrix Product Quiz
- 3. Matrix Product Optimal Order
- 4. Matrix Product
- 5. Some Interesting Linear Transforms
- 1. Linearity of Expectation
- 2. Variance of Sum
- 3. Random Variables and Covariance
- 4. Discrete Probability Practice
- 5. Maximal Likelihood of Normal Distribution
- 6. Maximal Likelihood of Multivariate Normal Distribution
- 1. PyTorch Getting Started
- 2. Gradient Descent Practice
- 3. MSE Gradient for Linear Regression
- 4. Pseudo-inverse Matrix Derivation
- 5. Spectral Geometry of Linear Regression
- 6. Careful Analysis of Linear Regression
- 7. Understanding Backpropagation
- 8. Coding Backpropagation
- 9. Training a Simple Network
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