A black box algorithm is one whose logic of is not interpretable by humans either due to its complexity or due to its logic being represented in a convoluted manner. On the other hand, a white box algorithm is one whose logic is visible and understandable for a human. In other words, explainability helps the human brain to understand why an algorithm is giving specific results. The degree of explainability is the measure to which a particular algorithm is understandable for the human brain. Many classes of algorithms, especially those related to machine learning, are classified as black box. If the algorithms are used for critical decision-making, it may be important to understand the reasons behind the results generated by the algorithm. Converting black box algorithms into white box ones also provides better insights into the...
40 Algorithms Every Programmer Should Know
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40 Algorithms Every Programmer Should Know
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Overview of this book
Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works.
You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.
By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
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Current Title:
40 Algorithms Every Programmer Should Know
Table of Contents (19 chapters)
Preface
Section 1: Fundamentals and Core Algorithms
Free Chapter
Overview of Algorithms
Data Structures Used in Algorithms
Sorting and Searching Algorithms
Designing Algorithms
Graph Algorithms
Section 2: Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Traditional Supervised Learning Algorithms
Neural Network Algorithms
Algorithms for Natural Language Processing
Recommendation Engines
Section 3: Advanced Topics
Data Algorithms
Cryptography
Large-Scale Algorithms
Practical Considerations
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