XeLabs/cs-self-learning
2
0
Fork 0
mirror of https://github.com/PKUFlyingPig/cs-self-learning.git synced 2026-06-23 09:58:12 +08:00
Code Issues Projects Releases Packages Wiki Activity

fix description

Browse source
This commit is contained in:
BarbarossaWang 2022-09-25 19:29:38 +08:00 committed by GitHub
parent 647abc210e
commit dd5cba1286
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
docs/数据结构与算法/Algo.en.md
View file

@ -10,13 +10,13 @@
This is the highest rated algorithms course on [Coursera](https://www.coursera.org), and Professor Robert Sedgewick has a magic way of making even the most complex algorithms look incredibly vivid and simple. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later.
Do you think that after learning the algorithm, then you forget it? I think the core of letting you fully grasp an algorithm lies in understanding three points:
Do you feel that you have learned algorithms then forget them quickly? I think the key of letting you fully grasp an algorithm lies in understanding three points:
- Why do you do this? (Correctness derivation, or the essence of the entire algorithm.)
- How to implement it? (It is not enough to learn without practices.)
- Use it to solve practical problems (Know how to apply you have learned is real skill.)
The composition of this course fits the three steps above very well. Watching the course videos and reading the professor's [textbook](https://algs4.cs.princeton.edu/home/) will help you understand the nature of the algorithm and allow you to tell others why the algorithm has to look like this in very vivid and simple terms .
The composition of this course fits the three points above very well. Watching the course videos and reading the professor's [textbook](https://algs4.cs.princeton.edu/home/) will help you understand the nature of the algorithm and allow you to tell others why the algorithm has to look like this in very vivid and simple terms .
After understanding algorithms, you can read the professor's [code implementation](https://algs4.cs.princeton.edu/code/) of all the data structures and algorithms taught in the course.
Note that these implementations are not demo, but industrial, efficient implementations. They are high-quality code that have rigorous annotations and variable names, and their modularization is also quite good. I learned a lot from these codes.