Solve classic computer science problems from fundamental algorithms, such as sorting and searching, to modern algorithms in machine learning and cryptography
Key Features
- More emphasis on modern deep learning techniques, including LSTMs, GRUs, and RNNs
- Newer topics include how to handle hidden bias in data and the explainability of the algorithms
- Explore different programming algorithms and choose the right data structures for their optimal implementation
Book Description
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 tackle problems in the real world by understanding 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.
Case studies on weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Then you will focus on deep learning algorithms and will learn about different types of deep learning models along with their practical use.
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 programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
What you will learn
- Design algorithms for solving complex problems
- Become familiar with neural networks and deep learning techniques
- Explore existing data structures and algorithms found in Python libraries
- Implement graph algorithms for fraud detection using network analysis
- Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time
- Create a recommendation engine that suggests relevant movies to subscribers
- Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform
Who This Book Is For
This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.
Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.
Python programming experience is a must, knowledge of data science will be helpful but not necessary.
Table of Contents
- Overview of Algorithms
- Data Structures Used in Algorithms
- Sorting and Searching Algorithms
- Designing Algorithms
- Graph Algorithms
- Unsupervised Machine Learning Algorithms
- Traditional Supervised Learning Algorithms
- Neural Network Algorithms
- Advanced Deep Learning Algorithms
- Algorithms for Natural Language Processing
- Recommendation Engines
- Data Algorithms
- Cryptography
- Large-Scale Algorithms
- Practical Considerations
