Mathematics in Deep Learning
Learn the mathematics behind deep learning, and see it work in code. This course holds the mathematical foundations of deep learning that are important to know. Every idea comes with exercises, so you do not only read the math, you use it. Tasks are quizzes, derivations and Python exercises with NumPy and PyTorch. You should know basic Python and the calculus from a first university year. Author: Mikhail Romanov, JetBrains. Found a bug or a typo, or have an idea for the course? Write to romanov.michael.v@gmail.com.
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
- 8. Gradient Descent Practice
- 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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Need more help? Check out our guide.
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