Lu, Yukuan

Useful self-hosted docker images which can be hosted on a remote server and be visited on the local browser.

1. Shiyu Zhao. Chapter 5: Monte Carlo Methods. Mathematical Foundations of Reinforcement Learning.
2. --> Youtube: MC Basic

Sources:

1. Shiyu Zhao. Chapter 5: Monte Carlo Methods. Mathematical Foundations of Reinforcement Learning
2. Shiyu Zhao. Chapter 6: Stochastic Approximation. Mathematical Foundations of Reinforcement Learning

Mean estimation: compute \(\mathbb{E}[X]\) using \(\left\{x_k\right\}\) \[ w_{k+1}=w_k-\frac{1}{k}\left(w_k-x_k\right) \]

Sources:

1. Stanford CS231, Lecture 6

Sources:

1. Derivatives, Backpropagation, and Vectorization
2. Wikipedia: chain rule
3. Wikipedia: quptient rule

It's recommended to compute the back propagation manually to get a deeper understanding. Read this article if you are interested.

//NOTE: This article is not finished yet and contains many errors. I am always ready to edit it.

Sources:

1. Thomas M. Cover & Joy A. Thomas. (2006). Chapter 8. Differential Entropy. Elements of Information Theory (2nd ed., pp. 243-255). Wiley-Interscience.
2. Fady Alajaji & Po-Ning Chen. (2018). Chapter 5. Differential Entropy and Gaussian Channels. An Introduction to Single-User Information Theory (1st ed., pp. 165-218). Springer.

//NOTE: This article is not finished yet and contains many errors. I am always ready to edit it.

Sources:

1. Thomas M. Cover & Joy A. Thomas. (2006). Chapter 8. Differential Entropy. Elements of Information Theory (2nd ed., pp. 243-255). Wiley-Interscience.
2. Fady Alajaji & Po-Ning Chen. (2018). Chapter 5. Differential Entropy and Gaussian Channels. An Introduction to Single-User Information Theory (1st ed., pp. 165-218). Springer.

Sources:

1. Thomas M. Cover & Joy A. Thomas. (2006). Chapter 8. Differential Entropy. Elements of Information Theory (2nd ed., pp. 243-255). Wiley-Interscience.
2. Fady Alajaji & Po-Ning Chen. (2018). Chapter 5. Differential Entropy and Gaussian Channels. An Introduction to Single-User Information Theory (1st ed., pp. 165-218). Springer.

Sources:

1. The Normal/Gaussian Random Variable

Sources:

1. VAE paper
2. Mathematics Behind Variational AutoEncoders
3. Variational autoencoders by Matthew N. Bernstein
4. From Autoencoder to Beta-VAE
5. Stanford CS 228: The variational auto-encoder

Link: My VAE implementation on Github