Algorithms for Dummies 2/e

THE ALGORITHM KNEW YOU NEEDED ALGORITHMS FOR DUMMIES

Algorithms For Dummies 2^nd Edition will explain the basics of what algorithms are, how they work, where they are in our lives, and how to create them yourself. Whether you're an internet user who is curious about the way algorithms affect your online habits, or a computer science student who wants to build a foundation in understanding algorithms, this book can get you started in the right direction.

UPDATES:

• Use pure Python and avoid third party libraries.
• Create a NumPy replacement in a separate chapter near the beginning that includes:

• • A matrix computation class for matrix operations
  • Special classes for certain operations, such stacking or queuing

• Get rid of Anaconda and use Google Colab exclusively (this would get rid of the compatibility messages and significantly reduce the amount of introductory materials--we could possibly get rid of Chapter 3 or at least make it much smaller).
• Modify the Downloading the Datasets and Example Code section of Chapter 3 specifically for Google Colab and place it at the end of Chapter 1 if we decide to get rid of Chapter 3.
• Create an online repository for the datasets and source code, likely using GitHub. This would allow us to provide updates to the source code when readers find errors and ensure that the right versions of the datasets remain available.
• Remove Chapter 4 entirely and point the reader to online tutorials instead (this was the most unpopular chapter in the book).
• Use more step-by-step instructions when possible.
• Add more graphics (I'm very good with mechanical type drawings and there is a lot to be said for graphs/charts).
• Use more real world/historical examples if possible (they're extremely popular).
• Add a chapter on logistic regression (quite a few of our readers have requested one since we already cover linear programming in Chapter 19).
• Add other useful algorithms if space allows.

「演算法白痴的演算法」是一本解釋演算法基礎知識、運作方式、在生活中的應用以及如何自己創建演算法的書籍。無論你是一個對演算法如何影響你的網路習慣感到好奇的網路使用者，還是一個想要建立對演算法理解基礎的計算機科學學生，這本書都能讓你朝著正確的方向開始。

更新內容:
- 使用純Python，避免使用第三方庫。
- 在開頭附近的一個單獨章節中創建一個NumPy替代品，包括:
- 用於矩陣運算的矩陣計算類
- 用於特定操作的特殊類，例如堆疊或排隊
- 捨棄Anaconda，僅使用Google Colab（這將消除兼容性訊息並顯著減少入門材料的量-我們可能可以捨棄第3章，或者至少將其縮小）。
- 將第3章的「下載數據集和示例代碼」部分修改為Google Colab專用，如果我們決定捨棄第3章，則將其放在第1章的末尾。
- 創建一個在線數據集和源代碼庫，可能使用GitHub。這將使我們能夠在讀者發現錯誤時提供源代碼的更新，並確保正確版本的數據集仍然可用。
- 完全刪除第4章，並將讀者指向在線教程（這是本書中最不受歡迎的章節）。
- 在可能的情況下，使用更多的逐步指示。
- 添加更多圖形（我在機械類型繪圖方面非常擅長，圖表非常受歡迎）。
- 如果可能，使用更多現實世界/歷史性的例子（它們非常受歡迎）。
- 添加一章關於邏輯回歸的內容（因為我們已經在第19章中涵蓋了線性規劃，所以有很多讀者要求這一章）。
- 如果空間允許，添加其他有用的演算法。

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

John Mueller已經出版了100多本關於科技、數據和編程的書籍。John擁有一個網站和博客，在那裡他寫關於科技的文章並提供與他出版的書籍相關的協助。

Luca Massaron是一位專注於保險和金融的數據科學家。作為一位Google開發者專家，他從2000年以來一直從事量化分析和算法。