陆昱宽

Sources:

1. UWashington: CSE471, Lecture04
2. UCSD: CSE240A, Lecture13
3. John L. Hennessy & David A. Patterson. (2019). Chapter 3. Instruction-Level Parallelism and Its Exploitation. Computer Architecture: A Quantitative Approach (6th ed., pp. 168-266). Elsevier Inc.

Sources:

1. UWashington: CSE378, Lecture13
2. John L. Hennessy & David A. Patterson. (2019). Chapter 1. Fundamentals of Quantitative Design and Analysis. Computer Architecture: A Quantitative Approach (6th ed.). Elsevier Inc.

Sources:

1. CMU CS651: P, NP, and NP-Completeness

Sources:

1. SSH Essentials: Working with SSH Servers, Clients, and Keys

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.